Unit Tests for curriculums - #41

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Unit Tests for curriculums#41
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@nmitra28nmitra28 commented May 10, 2024

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DomainRandomization LearningProgressCurriculum

other methods commented out due to failure.

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  1. DomainRandomization with seed! SUCCESSFUL
  2. DomainRandomization without seed! SUCCESSFUL
  3. LearningProgressCurriculum with seed! SUCCESSFUL
  4. LearningProgressCurriculum without seed! SUCCESSFUL
  5. SequentialCurriculum with seed! SUCCESSFUL
  6. SequentialCurriculum without seed! SUCCESSFUL
  7. CentralizedPrioritizedLevelReplay with seed! SUCCESSFUL
  8. CentralizedPrioritizedLevelReplay without seed! SUCCESSFUL
  9. PrioritizedLevelReplay with seed! SUCCESSFUL
  10. PrioritizedLevelReplay without seed! SUCCESSFUL

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python -m syllabus.examples.training_scripts.cleanrl_procgen_plr --env-id="bigfish" --seed=1 --num-envs=4 --num-eval-episodes=3

For these metrics the expected return is :

global_step=164, episodic_return=1.0
global_step=300, episodic_return=0.0
global_step=380, episodic_return=3.0
global_step=420, episodic_return=0.0
global_step=592, episodic_return=1.0
global_step=728, episodic_return=4.0
global_step=740, episodic_return=0.0
global_step=760, episodic_return=3.0
global_step=908, episodic_return=1.0
global_step=936, episodic_return=4.0
SPS: 63
global_step=1040, episodic_return=0.0
global_step=1140, episodic_return=3.0
global_step=1200, episodic_return=0.0
global_step=1348, episodic_return=0.0
global_step=1484, episodic_return=4.0
global_step=1496, episodic_return=0.0
global_step=1564, episodic_return=2.0
global_step=1684, episodic_return=0.0
global_step=1836, episodic_return=4.0
global_step=1844, episodic_return=0.0
global_step=1864, episodic_return=3.0
global_step=1984, episodic_return=0.0
SPS: 60
global_step=2140, episodic_return=0.0
global_step=2200, episodic_return=2.0
global_step=2264, episodic_return=0.0
global_step=2364, episodic_return=4.0
global_step=2404, episodic_return=0.0
global_step=2540, episodic_return=0.0
global_step=2580, episodic_return=3.0
global_step=2680, episodic_return=0.0
global_step=2812, episodic_return=6.0
global_step=2820, episodic_return=0.0
global_step=2964, episodic_return=3.0
global_step=2968, episodic_return=0.0
SPS: 60
global_step=3116, episodic_return=0.0
global_step=3160, episodic_return=4.0
global_step=3244, episodic_return=0.0
global_step=3320, episodic_return=3.0
global_step=3388, episodic_return=0.0
global_step=3512, episodic_return=0.0
global_step=3656, episodic_return=0.0
global_step=3704, episodic_return=3.0
global_step=3728, episodic_return=4.0
global_step=3812, episodic_return=0.0
global_step=3972, episodic_return=0.0
global_step=4000, episodic_return=4.0
global_step=4060, episodic_return=2.0
SPS: 61
global_step=4120, episodic_return=0.0
global_step=4260, episodic_return=0.0
global_step=4400, episodic_return=0.0
global_step=4440, episodic_return=3.0
global_step=4528, episodic_return=0.0
global_step=4676, episodic_return=0.0
global_step=4724, episodic_return=6.0
global_step=4760, episodic_return=3.0
global_step=4792, episodic_return=0.0
global_step=4820, episodic_return=3.0
global_step=4972, episodic_return=1.0
global_step=5108, episodic_return=0.0
SPS: 60
global_step=5180, episodic_return=3.0
global_step=5288, episodic_return=1.0
global_step=5400, episodic_return=0.0
global_step=5540, episodic_return=0.0
global_step=5564, episodic_return=3.0
global_step=5572, episodic_return=3.0
global_step=5676, episodic_return=0.0
global_step=5712, episodic_return=6.0
global_step=5816, episodic_return=0.0
global_step=5948, episodic_return=3.0
global_step=5976, episodic_return=0.0
global_step=6120, episodic_return=0.0
SPS: 60
global_step=6252, episodic_return=0.0
global_step=6328, episodic_return=3.0
global_step=6404, episodic_return=1.0
global_step=6412, episodic_return=4.0
global_step=6556, episodic_return=0.0
global_step=6676, episodic_return=4.0
global_step=6688, episodic_return=4.0
global_step=6700, episodic_return=0.0
global_step=6836, episodic_return=0.0
global_step=6964, episodic_return=0.0
global_step=7068, episodic_return=3.0
global_step=7136, episodic_return=1.0

.....................

@nmitra28
nmitra28force-pushed the nimitra/seedunittest branch from 7acc1f5 to 0bf2188CompareJuly 2, 2024 07:36
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nmitra28 commented Jul 2, 2024

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#11 DomainRandomization with seed with sample_weights
space = TaskSpace(gym.spaces.Discrete(4), ["a", "b", "c","d"])
c = DomainRandomization(task_space = space, seed = seed, sample_weights = [0.6,0.2,0.1,0.1])
if seed_test(c = c) :
print("DomainRandomization with seed with sample weights! SUCCESSFUL")
#12: DomainRandomization without seed
c = DomainRandomization(task_space = space, sample_weights = [0.3,0.2,0.4,0.1])
if no_seed_test(c = c) :
print("DomainRandomization without seed with sample weights! SUCCESSFUL")

[0]
[0]
[0]
[0]
[0]
DomainRandomization with seed with sample weights! SUCCESSFUL
[3]
[0]
[2]
[2]
[2]
DomainRandomization without seed with sample weights! SUCCESSFUL

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
Unit Tests for curriculums - #41
Open
nmitra28 wants to merge 5 commits into
RyanNavillus:mainfrom 
nmitra28:nimitra/seedunittest
Open
Unit Tests for curriculums#41
nmitra28 wants to merge 5 commits into
RyanNavillus:mainfrom 
nmitra28:nimitra/seedunittest
Conversation
@nmitra28
@nmitra28nmitra28
commented
May 10, 2024

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DomainRandomization LearningProgressCurriculum
other methods commented out due to failure.
@nmitra28
nmitra28
commented
May 15, 2024

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ContributorAuthor
  1. DomainRandomization with seed! SUCCESSFUL
  2. DomainRandomization without seed! SUCCESSFUL
  3. LearningProgressCurriculum with seed! SUCCESSFUL
  4. LearningProgressCurriculum without seed! SUCCESSFUL
  5. SequentialCurriculum with seed! SUCCESSFUL
  6. SequentialCurriculum without seed! SUCCESSFUL
  7. CentralizedPrioritizedLevelReplay with seed! SUCCESSFUL
  8. CentralizedPrioritizedLevelReplay without seed! SUCCESSFUL
  9. PrioritizedLevelReplay with seed! SUCCESSFUL
  10. PrioritizedLevelReplay without seed! SUCCESSFUL
@nmitra28
nmitra28
commented
Jun 29, 2024

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python -m syllabus.examples.training_scripts.cleanrl_procgen_plr --env-id="bigfish" --seed=1 --num-envs=4 --num-eval-episodes=3
For these metrics the expected return is :
global_step=164, episodic_return=1.0

global_step=300, episodic_return=0.0

global_step=380, episodic_return=3.0

global_step=420, episodic_return=0.0

global_step=592, episodic_return=1.0

global_step=728, episodic_return=4.0

global_step=740, episodic_return=0.0

global_step=760, episodic_return=3.0

global_step=908, episodic_return=1.0

global_step=936, episodic_return=4.0

SPS: 63

global_step=1040, episodic_return=0.0

global_step=1140, episodic_return=3.0

global_step=1200, episodic_return=0.0

global_step=1348, episodic_return=0.0

global_step=1484, episodic_return=4.0

global_step=1496, episodic_return=0.0

global_step=1564, episodic_return=2.0

global_step=1684, episodic_return=0.0

global_step=1836, episodic_return=4.0

global_step=1844, episodic_return=0.0

global_step=1864, episodic_return=3.0

global_step=1984, episodic_return=0.0

SPS: 60

global_step=2140, episodic_return=0.0

global_step=2200, episodic_return=2.0

global_step=2264, episodic_return=0.0

global_step=2364, episodic_return=4.0

global_step=2404, episodic_return=0.0

global_step=2540, episodic_return=0.0

global_step=2580, episodic_return=3.0

global_step=2680, episodic_return=0.0

global_step=2812, episodic_return=6.0

global_step=2820, episodic_return=0.0

global_step=2964, episodic_return=3.0

global_step=2968, episodic_return=0.0

SPS: 60

global_step=3116, episodic_return=0.0

global_step=3160, episodic_return=4.0

global_step=3244, episodic_return=0.0

global_step=3320, episodic_return=3.0

global_step=3388, episodic_return=0.0

global_step=3512, episodic_return=0.0

global_step=3656, episodic_return=0.0

global_step=3704, episodic_return=3.0

global_step=3728, episodic_return=4.0

global_step=3812, episodic_return=0.0

global_step=3972, episodic_return=0.0

global_step=4000, episodic_return=4.0

global_step=4060, episodic_return=2.0

SPS: 61

global_step=4120, episodic_return=0.0

global_step=4260, episodic_return=0.0

global_step=4400, episodic_return=0.0

global_step=4440, episodic_return=3.0

global_step=4528, episodic_return=0.0

global_step=4676, episodic_return=0.0

global_step=4724, episodic_return=6.0

global_step=4760, episodic_return=3.0

global_step=4792, episodic_return=0.0

global_step=4820, episodic_return=3.0

global_step=4972, episodic_return=1.0

global_step=5108, episodic_return=0.0

SPS: 60

global_step=5180, episodic_return=3.0

global_step=5288, episodic_return=1.0

global_step=5400, episodic_return=0.0

global_step=5540, episodic_return=0.0

global_step=5564, episodic_return=3.0

global_step=5572, episodic_return=3.0

global_step=5676, episodic_return=0.0

global_step=5712, episodic_return=6.0

global_step=5816, episodic_return=0.0

global_step=5948, episodic_return=3.0

global_step=5976, episodic_return=0.0

global_step=6120, episodic_return=0.0

SPS: 60

global_step=6252, episodic_return=0.0

global_step=6328, episodic_return=3.0

global_step=6404, episodic_return=1.0

global_step=6412, episodic_return=4.0

global_step=6556, episodic_return=0.0

global_step=6676, episodic_return=4.0

global_step=6688, episodic_return=4.0

global_step=6700, episodic_return=0.0

global_step=6836, episodic_return=0.0

global_step=6964, episodic_return=0.0

global_step=7068, episodic_return=3.0

global_step=7136, episodic_return=1.0
.....................
@nmitra28
nmitra28force-pushed
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0bf2188CompareJuly 2, 2024 07:36
@nmitra28
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#11 DomainRandomization with seed with sample_weights
space = TaskSpace(gym.spaces.Discrete(4), ["a", "b", "c","d"])
c = DomainRandomization(task_space = space, seed = seed, sample_weights = [0.6,0.2,0.1,0.1])
if seed_test(c = c) :
print("DomainRandomization with seed with sample weights! SUCCESSFUL")
#12: DomainRandomization without seed
c = DomainRandomization(task_space = space, sample_weights = [0.3,0.2,0.4,0.1])
if no_seed_test(c = c) :
print("DomainRandomization without seed with sample weights! SUCCESSFUL")

[0]

[0]

[0]

[0]

[0]

DomainRandomization with seed with sample weights! SUCCESSFUL

[3]

[0]

[2]

[2]

[2]

DomainRandomization without seed with sample weights! SUCCESSFUL
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Unit Tests for curriculums - #41
Open
nmitra28 wants to merge 5 commits into
RyanNavillus:mainfrom 
nmitra28:nimitra/seedunittest
Open
Unit Tests for curriculums#41
nmitra28 wants to merge 5 commits into
RyanNavillus:mainfrom 
nmitra28:nimitra/seedunittest
Conversation
@nmitra28
@nmitra28nmitra28
commented
May 10, 2024

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DomainRandomization LearningProgressCurriculum
other methods commented out due to failure.
@nmitra28
nmitra28
commented
May 15, 2024

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ContributorAuthor
  1. DomainRandomization with seed! SUCCESSFUL
  2. DomainRandomization without seed! SUCCESSFUL
  3. LearningProgressCurriculum with seed! SUCCESSFUL
  4. LearningProgressCurriculum without seed! SUCCESSFUL
  5. SequentialCurriculum with seed! SUCCESSFUL
  6. SequentialCurriculum without seed! SUCCESSFUL
  7. CentralizedPrioritizedLevelReplay with seed! SUCCESSFUL
  8. CentralizedPrioritizedLevelReplay without seed! SUCCESSFUL
  9. PrioritizedLevelReplay with seed! SUCCESSFUL
  10. PrioritizedLevelReplay without seed! SUCCESSFUL
@nmitra28
nmitra28
commented
Jun 29, 2024

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python -m syllabus.examples.training_scripts.cleanrl_procgen_plr --env-id="bigfish" --seed=1 --num-envs=4 --num-eval-episodes=3
For these metrics the expected return is :
global_step=164, episodic_return=1.0

global_step=300, episodic_return=0.0

global_step=380, episodic_return=3.0

global_step=420, episodic_return=0.0

global_step=592, episodic_return=1.0

global_step=728, episodic_return=4.0

global_step=740, episodic_return=0.0

global_step=760, episodic_return=3.0

global_step=908, episodic_return=1.0

global_step=936, episodic_return=4.0

SPS: 63

global_step=1040, episodic_return=0.0

global_step=1140, episodic_return=3.0

global_step=1200, episodic_return=0.0

global_step=1348, episodic_return=0.0

global_step=1484, episodic_return=4.0

global_step=1496, episodic_return=0.0

global_step=1564, episodic_return=2.0

global_step=1684, episodic_return=0.0

global_step=1836, episodic_return=4.0

global_step=1844, episodic_return=0.0

global_step=1864, episodic_return=3.0

global_step=1984, episodic_return=0.0

SPS: 60

global_step=2140, episodic_return=0.0

global_step=2200, episodic_return=2.0

global_step=2264, episodic_return=0.0

global_step=2364, episodic_return=4.0

global_step=2404, episodic_return=0.0

global_step=2540, episodic_return=0.0

global_step=2580, episodic_return=3.0

global_step=2680, episodic_return=0.0

global_step=2812, episodic_return=6.0

global_step=2820, episodic_return=0.0

global_step=2964, episodic_return=3.0

global_step=2968, episodic_return=0.0

SPS: 60

global_step=3116, episodic_return=0.0

global_step=3160, episodic_return=4.0

global_step=3244, episodic_return=0.0

global_step=3320, episodic_return=3.0

global_step=3388, episodic_return=0.0

global_step=3512, episodic_return=0.0

global_step=3656, episodic_return=0.0

global_step=3704, episodic_return=3.0

global_step=3728, episodic_return=4.0

global_step=3812, episodic_return=0.0

global_step=3972, episodic_return=0.0

global_step=4000, episodic_return=4.0

global_step=4060, episodic_return=2.0

SPS: 61

global_step=4120, episodic_return=0.0

global_step=4260, episodic_return=0.0

global_step=4400, episodic_return=0.0

global_step=4440, episodic_return=3.0

global_step=4528, episodic_return=0.0

global_step=4676, episodic_return=0.0

global_step=4724, episodic_return=6.0

global_step=4760, episodic_return=3.0

global_step=4792, episodic_return=0.0

global_step=4820, episodic_return=3.0

global_step=4972, episodic_return=1.0

global_step=5108, episodic_return=0.0

SPS: 60

global_step=5180, episodic_return=3.0

global_step=5288, episodic_return=1.0

global_step=5400, episodic_return=0.0

global_step=5540, episodic_return=0.0

global_step=5564, episodic_return=3.0

global_step=5572, episodic_return=3.0

global_step=5676, episodic_return=0.0

global_step=5712, episodic_return=6.0

global_step=5816, episodic_return=0.0

global_step=5948, episodic_return=3.0

global_step=5976, episodic_return=0.0

global_step=6120, episodic_return=0.0

SPS: 60

global_step=6252, episodic_return=0.0

global_step=6328, episodic_return=3.0

global_step=6404, episodic_return=1.0

global_step=6412, episodic_return=4.0

global_step=6556, episodic_return=0.0

global_step=6676, episodic_return=4.0

global_step=6688, episodic_return=4.0

global_step=6700, episodic_return=0.0

global_step=6836, episodic_return=0.0

global_step=6964, episodic_return=0.0

global_step=7068, episodic_return=3.0

global_step=7136, episodic_return=1.0
.....................
@nmitra28
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@nmitra28
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#11 DomainRandomization with seed with sample_weights
space = TaskSpace(gym.spaces.Discrete(4), ["a", "b", "c","d"])
c = DomainRandomization(task_space = space, seed = seed, sample_weights = [0.6,0.2,0.1,0.1])
if seed_test(c = c) :
print("DomainRandomization with seed with sample weights! SUCCESSFUL")
#12: DomainRandomization without seed
c = DomainRandomization(task_space = space, sample_weights = [0.3,0.2,0.4,0.1])
if no_seed_test(c = c) :
print("DomainRandomization without seed with sample weights! SUCCESSFUL")

[0]

[0]

[0]

[0]

[0]

DomainRandomization with seed with sample weights! SUCCESSFUL

[3]

[0]

[2]

[2]

[2]

DomainRandomization without seed with sample weights! SUCCESSFUL
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Unit Tests for curriculums - #41
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Unit Tests for curriculums#41
nmitra28 wants to merge 5 commits into
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@nmitra28
@nmitra28nmitra28
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May 10, 2024

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DomainRandomization LearningProgressCurriculum
other methods commented out due to failure.
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May 15, 2024

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  1. DomainRandomization with seed! SUCCESSFUL
  2. DomainRandomization without seed! SUCCESSFUL
  3. LearningProgressCurriculum with seed! SUCCESSFUL
  4. LearningProgressCurriculum without seed! SUCCESSFUL
  5. SequentialCurriculum with seed! SUCCESSFUL
  6. SequentialCurriculum without seed! SUCCESSFUL
  7. CentralizedPrioritizedLevelReplay with seed! SUCCESSFUL
  8. CentralizedPrioritizedLevelReplay without seed! SUCCESSFUL
  9. PrioritizedLevelReplay with seed! SUCCESSFUL
  10. PrioritizedLevelReplay without seed! SUCCESSFUL
@nmitra28
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Jun 29, 2024

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python -m syllabus.examples.training_scripts.cleanrl_procgen_plr --env-id="bigfish" --seed=1 --num-envs=4 --num-eval-episodes=3
For these metrics the expected return is :
global_step=164, episodic_return=1.0

global_step=300, episodic_return=0.0

global_step=380, episodic_return=3.0

global_step=420, episodic_return=0.0

global_step=592, episodic_return=1.0

global_step=728, episodic_return=4.0

global_step=740, episodic_return=0.0

global_step=760, episodic_return=3.0

global_step=908, episodic_return=1.0

global_step=936, episodic_return=4.0

SPS: 63

global_step=1040, episodic_return=0.0

global_step=1140, episodic_return=3.0

global_step=1200, episodic_return=0.0

global_step=1348, episodic_return=0.0

global_step=1484, episodic_return=4.0

global_step=1496, episodic_return=0.0

global_step=1564, episodic_return=2.0

global_step=1684, episodic_return=0.0

global_step=1836, episodic_return=4.0

global_step=1844, episodic_return=0.0

global_step=1864, episodic_return=3.0

global_step=1984, episodic_return=0.0

SPS: 60

global_step=2140, episodic_return=0.0

global_step=2200, episodic_return=2.0

global_step=2264, episodic_return=0.0

global_step=2364, episodic_return=4.0

global_step=2404, episodic_return=0.0

global_step=2540, episodic_return=0.0

global_step=2580, episodic_return=3.0

global_step=2680, episodic_return=0.0

global_step=2812, episodic_return=6.0

global_step=2820, episodic_return=0.0

global_step=2964, episodic_return=3.0

global_step=2968, episodic_return=0.0

SPS: 60

global_step=3116, episodic_return=0.0

global_step=3160, episodic_return=4.0

global_step=3244, episodic_return=0.0

global_step=3320, episodic_return=3.0

global_step=3388, episodic_return=0.0

global_step=3512, episodic_return=0.0

global_step=3656, episodic_return=0.0

global_step=3704, episodic_return=3.0

global_step=3728, episodic_return=4.0

global_step=3812, episodic_return=0.0

global_step=3972, episodic_return=0.0

global_step=4000, episodic_return=4.0

global_step=4060, episodic_return=2.0

SPS: 61

global_step=4120, episodic_return=0.0

global_step=4260, episodic_return=0.0

global_step=4400, episodic_return=0.0

global_step=4440, episodic_return=3.0

global_step=4528, episodic_return=0.0

global_step=4676, episodic_return=0.0

global_step=4724, episodic_return=6.0

global_step=4760, episodic_return=3.0

global_step=4792, episodic_return=0.0

global_step=4820, episodic_return=3.0

global_step=4972, episodic_return=1.0

global_step=5108, episodic_return=0.0

SPS: 60

global_step=5180, episodic_return=3.0

global_step=5288, episodic_return=1.0

global_step=5400, episodic_return=0.0

global_step=5540, episodic_return=0.0

global_step=5564, episodic_return=3.0

global_step=5572, episodic_return=3.0

global_step=5676, episodic_return=0.0

global_step=5712, episodic_return=6.0

global_step=5816, episodic_return=0.0

global_step=5948, episodic_return=3.0

global_step=5976, episodic_return=0.0

global_step=6120, episodic_return=0.0

SPS: 60

global_step=6252, episodic_return=0.0

global_step=6328, episodic_return=3.0

global_step=6404, episodic_return=1.0

global_step=6412, episodic_return=4.0

global_step=6556, episodic_return=0.0

global_step=6676, episodic_return=4.0

global_step=6688, episodic_return=4.0

global_step=6700, episodic_return=0.0

global_step=6836, episodic_return=0.0

global_step=6964, episodic_return=0.0

global_step=7068, episodic_return=3.0

global_step=7136, episodic_return=1.0
.....................
@nmitra28
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0bf2188CompareJuly 2, 2024 07:36
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#11 DomainRandomization with seed with sample_weights
space = TaskSpace(gym.spaces.Discrete(4), ["a", "b", "c","d"])
c = DomainRandomization(task_space = space, seed = seed, sample_weights = [0.6,0.2,0.1,0.1])
if seed_test(c = c) :
print("DomainRandomization with seed with sample weights! SUCCESSFUL")
#12: DomainRandomization without seed
c = DomainRandomization(task_space = space, sample_weights = [0.3,0.2,0.4,0.1])
if no_seed_test(c = c) :
print("DomainRandomization without seed with sample weights! SUCCESSFUL")

[0]

[0]

[0]

[0]

[0]

DomainRandomization with seed with sample weights! SUCCESSFUL

[3]

[0]

[2]

[2]

[2]

DomainRandomization without seed with sample weights! SUCCESSFUL
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
Unit Tests for curriculums - #41
Open
nmitra28 wants to merge 5 commits into
RyanNavillus:mainfrom 
nmitra28:nimitra/seedunittest
Open
Unit Tests for curriculums#41
nmitra28 wants to merge 5 commits into
RyanNavillus:mainfrom 
nmitra28:nimitra/seedunittest
Conversation
@nmitra28
@nmitra28nmitra28
commented
May 10, 2024

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Contributor
DomainRandomization LearningProgressCurriculum
other methods commented out due to failure.
@nmitra28
nmitra28
commented
May 15, 2024

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ContributorAuthor
  1. DomainRandomization with seed! SUCCESSFUL
  2. DomainRandomization without seed! SUCCESSFUL
  3. LearningProgressCurriculum with seed! SUCCESSFUL
  4. LearningProgressCurriculum without seed! SUCCESSFUL
  5. SequentialCurriculum with seed! SUCCESSFUL
  6. SequentialCurriculum without seed! SUCCESSFUL
  7. CentralizedPrioritizedLevelReplay with seed! SUCCESSFUL
  8. CentralizedPrioritizedLevelReplay without seed! SUCCESSFUL
  9. PrioritizedLevelReplay with seed! SUCCESSFUL
  10. PrioritizedLevelReplay without seed! SUCCESSFUL
@nmitra28
nmitra28
commented
Jun 29, 2024

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python -m syllabus.examples.training_scripts.cleanrl_procgen_plr --env-id="bigfish" --seed=1 --num-envs=4 --num-eval-episodes=3
For these metrics the expected return is :
global_step=164, episodic_return=1.0

global_step=300, episodic_return=0.0

global_step=380, episodic_return=3.0

global_step=420, episodic_return=0.0

global_step=592, episodic_return=1.0

global_step=728, episodic_return=4.0

global_step=740, episodic_return=0.0

global_step=760, episodic_return=3.0

global_step=908, episodic_return=1.0

global_step=936, episodic_return=4.0

SPS: 63

global_step=1040, episodic_return=0.0

global_step=1140, episodic_return=3.0

global_step=1200, episodic_return=0.0

global_step=1348, episodic_return=0.0

global_step=1484, episodic_return=4.0

global_step=1496, episodic_return=0.0

global_step=1564, episodic_return=2.0

global_step=1684, episodic_return=0.0

global_step=1836, episodic_return=4.0

global_step=1844, episodic_return=0.0

global_step=1864, episodic_return=3.0

global_step=1984, episodic_return=0.0

SPS: 60

global_step=2140, episodic_return=0.0

global_step=2200, episodic_return=2.0

global_step=2264, episodic_return=0.0

global_step=2364, episodic_return=4.0

global_step=2404, episodic_return=0.0

global_step=2540, episodic_return=0.0

global_step=2580, episodic_return=3.0

global_step=2680, episodic_return=0.0

global_step=2812, episodic_return=6.0

global_step=2820, episodic_return=0.0

global_step=2964, episodic_return=3.0

global_step=2968, episodic_return=0.0

SPS: 60

global_step=3116, episodic_return=0.0

global_step=3160, episodic_return=4.0

global_step=3244, episodic_return=0.0

global_step=3320, episodic_return=3.0

global_step=3388, episodic_return=0.0

global_step=3512, episodic_return=0.0

global_step=3656, episodic_return=0.0

global_step=3704, episodic_return=3.0

global_step=3728, episodic_return=4.0

global_step=3812, episodic_return=0.0

global_step=3972, episodic_return=0.0

global_step=4000, episodic_return=4.0

global_step=4060, episodic_return=2.0

SPS: 61

global_step=4120, episodic_return=0.0

global_step=4260, episodic_return=0.0

global_step=4400, episodic_return=0.0

global_step=4440, episodic_return=3.0

global_step=4528, episodic_return=0.0

global_step=4676, episodic_return=0.0

global_step=4724, episodic_return=6.0

global_step=4760, episodic_return=3.0

global_step=4792, episodic_return=0.0

global_step=4820, episodic_return=3.0

global_step=4972, episodic_return=1.0

global_step=5108, episodic_return=0.0

SPS: 60

global_step=5180, episodic_return=3.0

global_step=5288, episodic_return=1.0

global_step=5400, episodic_return=0.0

global_step=5540, episodic_return=0.0

global_step=5564, episodic_return=3.0

global_step=5572, episodic_return=3.0

global_step=5676, episodic_return=0.0

global_step=5712, episodic_return=6.0

global_step=5816, episodic_return=0.0

global_step=5948, episodic_return=3.0

global_step=5976, episodic_return=0.0

global_step=6120, episodic_return=0.0

SPS: 60

global_step=6252, episodic_return=0.0

global_step=6328, episodic_return=3.0

global_step=6404, episodic_return=1.0

global_step=6412, episodic_return=4.0

global_step=6556, episodic_return=0.0

global_step=6676, episodic_return=4.0

global_step=6688, episodic_return=4.0

global_step=6700, episodic_return=0.0

global_step=6836, episodic_return=0.0

global_step=6964, episodic_return=0.0

global_step=7068, episodic_return=3.0

global_step=7136, episodic_return=1.0
.....................
@nmitra28
nmitra28force-pushed
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0bf2188CompareJuly 2, 2024 07:36
@nmitra28
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#11 DomainRandomization with seed with sample_weights
space = TaskSpace(gym.spaces.Discrete(4), ["a", "b", "c","d"])
c = DomainRandomization(task_space = space, seed = seed, sample_weights = [0.6,0.2,0.1,0.1])
if seed_test(c = c) :
print("DomainRandomization with seed with sample weights! SUCCESSFUL")
#12: DomainRandomization without seed
c = DomainRandomization(task_space = space, sample_weights = [0.3,0.2,0.4,0.1])
if no_seed_test(c = c) :
print("DomainRandomization without seed with sample weights! SUCCESSFUL")

[0]

[0]

[0]

[0]

[0]

DomainRandomization with seed with sample weights! SUCCESSFUL

[3]

[0]

[2]

[2]

[2]

DomainRandomization without seed with sample weights! SUCCESSFUL
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Unit Tests for curriculums - #41
Open
nmitra28 wants to merge 5 commits into
RyanNavillus:mainfrom 
nmitra28:nimitra/seedunittest
Open
Unit Tests for curriculums#41
nmitra28 wants to merge 5 commits into
RyanNavillus:mainfrom 
nmitra28:nimitra/seedunittest
Conversation
@nmitra28
@nmitra28nmitra28
commented
May 10, 2024

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Contributor
DomainRandomization LearningProgressCurriculum
other methods commented out due to failure.
@nmitra28
nmitra28
commented
May 15, 2024

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ContributorAuthor
  1. DomainRandomization with seed! SUCCESSFUL
  2. DomainRandomization without seed! SUCCESSFUL
  3. LearningProgressCurriculum with seed! SUCCESSFUL
  4. LearningProgressCurriculum without seed! SUCCESSFUL
  5. SequentialCurriculum with seed! SUCCESSFUL
  6. SequentialCurriculum without seed! SUCCESSFUL
  7. CentralizedPrioritizedLevelReplay with seed! SUCCESSFUL
  8. CentralizedPrioritizedLevelReplay without seed! SUCCESSFUL
  9. PrioritizedLevelReplay with seed! SUCCESSFUL
  10. PrioritizedLevelReplay without seed! SUCCESSFUL
@nmitra28
nmitra28
commented
Jun 29, 2024

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python -m syllabus.examples.training_scripts.cleanrl_procgen_plr --env-id="bigfish" --seed=1 --num-envs=4 --num-eval-episodes=3
For these metrics the expected return is :
global_step=164, episodic_return=1.0

global_step=300, episodic_return=0.0

global_step=380, episodic_return=3.0

global_step=420, episodic_return=0.0

global_step=592, episodic_return=1.0

global_step=728, episodic_return=4.0

global_step=740, episodic_return=0.0

global_step=760, episodic_return=3.0

global_step=908, episodic_return=1.0

global_step=936, episodic_return=4.0

SPS: 63

global_step=1040, episodic_return=0.0

global_step=1140, episodic_return=3.0

global_step=1200, episodic_return=0.0

global_step=1348, episodic_return=0.0

global_step=1484, episodic_return=4.0

global_step=1496, episodic_return=0.0

global_step=1564, episodic_return=2.0

global_step=1684, episodic_return=0.0

global_step=1836, episodic_return=4.0

global_step=1844, episodic_return=0.0

global_step=1864, episodic_return=3.0

global_step=1984, episodic_return=0.0

SPS: 60

global_step=2140, episodic_return=0.0

global_step=2200, episodic_return=2.0

global_step=2264, episodic_return=0.0

global_step=2364, episodic_return=4.0

global_step=2404, episodic_return=0.0

global_step=2540, episodic_return=0.0

global_step=2580, episodic_return=3.0

global_step=2680, episodic_return=0.0

global_step=2812, episodic_return=6.0

global_step=2820, episodic_return=0.0

global_step=2964, episodic_return=3.0

global_step=2968, episodic_return=0.0

SPS: 60

global_step=3116, episodic_return=0.0

global_step=3160, episodic_return=4.0

global_step=3244, episodic_return=0.0

global_step=3320, episodic_return=3.0

global_step=3388, episodic_return=0.0

global_step=3512, episodic_return=0.0

global_step=3656, episodic_return=0.0

global_step=3704, episodic_return=3.0

global_step=3728, episodic_return=4.0

global_step=3812, episodic_return=0.0

global_step=3972, episodic_return=0.0

global_step=4000, episodic_return=4.0

global_step=4060, episodic_return=2.0

SPS: 61

global_step=4120, episodic_return=0.0

global_step=4260, episodic_return=0.0

global_step=4400, episodic_return=0.0

global_step=4440, episodic_return=3.0

global_step=4528, episodic_return=0.0

global_step=4676, episodic_return=0.0

global_step=4724, episodic_return=6.0

global_step=4760, episodic_return=3.0

global_step=4792, episodic_return=0.0

global_step=4820, episodic_return=3.0

global_step=4972, episodic_return=1.0

global_step=5108, episodic_return=0.0

SPS: 60

global_step=5180, episodic_return=3.0

global_step=5288, episodic_return=1.0

global_step=5400, episodic_return=0.0

global_step=5540, episodic_return=0.0

global_step=5564, episodic_return=3.0

global_step=5572, episodic_return=3.0

global_step=5676, episodic_return=0.0

global_step=5712, episodic_return=6.0

global_step=5816, episodic_return=0.0

global_step=5948, episodic_return=3.0

global_step=5976, episodic_return=0.0

global_step=6120, episodic_return=0.0

SPS: 60

global_step=6252, episodic_return=0.0

global_step=6328, episodic_return=3.0

global_step=6404, episodic_return=1.0

global_step=6412, episodic_return=4.0

global_step=6556, episodic_return=0.0

global_step=6676, episodic_return=4.0

global_step=6688, episodic_return=4.0

global_step=6700, episodic_return=0.0

global_step=6836, episodic_return=0.0

global_step=6964, episodic_return=0.0

global_step=7068, episodic_return=3.0

global_step=7136, episodic_return=1.0
.....................
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#11 DomainRandomization with seed with sample_weights
space = TaskSpace(gym.spaces.Discrete(4), ["a", "b", "c","d"])
c = DomainRandomization(task_space = space, seed = seed, sample_weights = [0.6,0.2,0.1,0.1])
if seed_test(c = c) :
print("DomainRandomization with seed with sample weights! SUCCESSFUL")
#12: DomainRandomization without seed
c = DomainRandomization(task_space = space, sample_weights = [0.3,0.2,0.4,0.1])
if no_seed_test(c = c) :
print("DomainRandomization without seed with sample weights! SUCCESSFUL")

[0]

[0]

[0]

[0]

[0]

DomainRandomization with seed with sample weights! SUCCESSFUL

[3]

[0]

[2]

[2]

[2]

DomainRandomization without seed with sample weights! SUCCESSFUL
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Unit Tests for curriculums - #41
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Unit Tests for curriculums#41
nmitra28 wants to merge 5 commits into
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@nmitra28
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May 10, 2024

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DomainRandomization LearningProgressCurriculum
other methods commented out due to failure.
@nmitra28
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May 15, 2024

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  1. DomainRandomization with seed! SUCCESSFUL
  2. DomainRandomization without seed! SUCCESSFUL
  3. LearningProgressCurriculum with seed! SUCCESSFUL
  4. LearningProgressCurriculum without seed! SUCCESSFUL
  5. SequentialCurriculum with seed! SUCCESSFUL
  6. SequentialCurriculum without seed! SUCCESSFUL
  7. CentralizedPrioritizedLevelReplay with seed! SUCCESSFUL
  8. CentralizedPrioritizedLevelReplay without seed! SUCCESSFUL
  9. PrioritizedLevelReplay with seed! SUCCESSFUL
  10. PrioritizedLevelReplay without seed! SUCCESSFUL
@nmitra28
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python -m syllabus.examples.training_scripts.cleanrl_procgen_plr --env-id="bigfish" --seed=1 --num-envs=4 --num-eval-episodes=3
For these metrics the expected return is :
global_step=164, episodic_return=1.0

global_step=300, episodic_return=0.0

global_step=380, episodic_return=3.0

global_step=420, episodic_return=0.0

global_step=592, episodic_return=1.0

global_step=728, episodic_return=4.0

global_step=740, episodic_return=0.0

global_step=760, episodic_return=3.0

global_step=908, episodic_return=1.0

global_step=936, episodic_return=4.0

SPS: 63

global_step=1040, episodic_return=0.0

global_step=1140, episodic_return=3.0

global_step=1200, episodic_return=0.0

global_step=1348, episodic_return=0.0

global_step=1484, episodic_return=4.0

global_step=1496, episodic_return=0.0

global_step=1564, episodic_return=2.0

global_step=1684, episodic_return=0.0

global_step=1836, episodic_return=4.0

global_step=1844, episodic_return=0.0

global_step=1864, episodic_return=3.0

global_step=1984, episodic_return=0.0

SPS: 60

global_step=2140, episodic_return=0.0

global_step=2200, episodic_return=2.0

global_step=2264, episodic_return=0.0

global_step=2364, episodic_return=4.0

global_step=2404, episodic_return=0.0

global_step=2540, episodic_return=0.0

global_step=2580, episodic_return=3.0

global_step=2680, episodic_return=0.0

global_step=2812, episodic_return=6.0

global_step=2820, episodic_return=0.0

global_step=2964, episodic_return=3.0

global_step=2968, episodic_return=0.0

SPS: 60

global_step=3116, episodic_return=0.0

global_step=3160, episodic_return=4.0

global_step=3244, episodic_return=0.0

global_step=3320, episodic_return=3.0

global_step=3388, episodic_return=0.0

global_step=3512, episodic_return=0.0

global_step=3656, episodic_return=0.0

global_step=3704, episodic_return=3.0

global_step=3728, episodic_return=4.0

global_step=3812, episodic_return=0.0

global_step=3972, episodic_return=0.0

global_step=4000, episodic_return=4.0

global_step=4060, episodic_return=2.0

SPS: 61

global_step=4120, episodic_return=0.0

global_step=4260, episodic_return=0.0

global_step=4400, episodic_return=0.0

global_step=4440, episodic_return=3.0

global_step=4528, episodic_return=0.0

global_step=4676, episodic_return=0.0

global_step=4724, episodic_return=6.0

global_step=4760, episodic_return=3.0

global_step=4792, episodic_return=0.0

global_step=4820, episodic_return=3.0

global_step=4972, episodic_return=1.0

global_step=5108, episodic_return=0.0

SPS: 60

global_step=5180, episodic_return=3.0

global_step=5288, episodic_return=1.0

global_step=5400, episodic_return=0.0

global_step=5540, episodic_return=0.0

global_step=5564, episodic_return=3.0

global_step=5572, episodic_return=3.0

global_step=5676, episodic_return=0.0

global_step=5712, episodic_return=6.0

global_step=5816, episodic_return=0.0

global_step=5948, episodic_return=3.0

global_step=5976, episodic_return=0.0

global_step=6120, episodic_return=0.0

SPS: 60

global_step=6252, episodic_return=0.0

global_step=6328, episodic_return=3.0

global_step=6404, episodic_return=1.0

global_step=6412, episodic_return=4.0

global_step=6556, episodic_return=0.0

global_step=6676, episodic_return=4.0

global_step=6688, episodic_return=4.0

global_step=6700, episodic_return=0.0

global_step=6836, episodic_return=0.0

global_step=6964, episodic_return=0.0

global_step=7068, episodic_return=3.0

global_step=7136, episodic_return=1.0
.....................
@nmitra28
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#11 DomainRandomization with seed with sample_weights
space = TaskSpace(gym.spaces.Discrete(4), ["a", "b", "c","d"])
c = DomainRandomization(task_space = space, seed = seed, sample_weights = [0.6,0.2,0.1,0.1])
if seed_test(c = c) :
print("DomainRandomization with seed with sample weights! SUCCESSFUL")
#12: DomainRandomization without seed
c = DomainRandomization(task_space = space, sample_weights = [0.3,0.2,0.4,0.1])
if no_seed_test(c = c) :
print("DomainRandomization without seed with sample weights! SUCCESSFUL")

[0]

[0]

[0]

[0]

[0]

DomainRandomization with seed with sample weights! SUCCESSFUL

[3]

[0]

[2]

[2]

[2]

DomainRandomization without seed with sample weights! SUCCESSFUL
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
Unit Tests for curriculums - #41
Open
nmitra28 wants to merge 5 commits into
RyanNavillus:mainfrom 
nmitra28:nimitra/seedunittest
Open
Unit Tests for curriculums#41
nmitra28 wants to merge 5 commits into
RyanNavillus:mainfrom 
nmitra28:nimitra/seedunittest
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@nmitra28
@nmitra28nmitra28
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DomainRandomization LearningProgressCurriculum
other methods commented out due to failure.
@nmitra28
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May 15, 2024

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  1. DomainRandomization with seed! SUCCESSFUL
  2. DomainRandomization without seed! SUCCESSFUL
  3. LearningProgressCurriculum with seed! SUCCESSFUL
  4. LearningProgressCurriculum without seed! SUCCESSFUL
  5. SequentialCurriculum with seed! SUCCESSFUL
  6. SequentialCurriculum without seed! SUCCESSFUL
  7. CentralizedPrioritizedLevelReplay with seed! SUCCESSFUL
  8. CentralizedPrioritizedLevelReplay without seed! SUCCESSFUL
  9. PrioritizedLevelReplay with seed! SUCCESSFUL
  10. PrioritizedLevelReplay without seed! SUCCESSFUL
@nmitra28
nmitra28
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python -m syllabus.examples.training_scripts.cleanrl_procgen_plr --env-id="bigfish" --seed=1 --num-envs=4 --num-eval-episodes=3
For these metrics the expected return is :
global_step=164, episodic_return=1.0

global_step=300, episodic_return=0.0

global_step=380, episodic_return=3.0

global_step=420, episodic_return=0.0

global_step=592, episodic_return=1.0

global_step=728, episodic_return=4.0

global_step=740, episodic_return=0.0

global_step=760, episodic_return=3.0

global_step=908, episodic_return=1.0

global_step=936, episodic_return=4.0

SPS: 63

global_step=1040, episodic_return=0.0

global_step=1140, episodic_return=3.0

global_step=1200, episodic_return=0.0

global_step=1348, episodic_return=0.0

global_step=1484, episodic_return=4.0

global_step=1496, episodic_return=0.0

global_step=1564, episodic_return=2.0

global_step=1684, episodic_return=0.0

global_step=1836, episodic_return=4.0

global_step=1844, episodic_return=0.0

global_step=1864, episodic_return=3.0

global_step=1984, episodic_return=0.0

SPS: 60

global_step=2140, episodic_return=0.0

global_step=2200, episodic_return=2.0

global_step=2264, episodic_return=0.0

global_step=2364, episodic_return=4.0

global_step=2404, episodic_return=0.0

global_step=2540, episodic_return=0.0

global_step=2580, episodic_return=3.0

global_step=2680, episodic_return=0.0

global_step=2812, episodic_return=6.0

global_step=2820, episodic_return=0.0

global_step=2964, episodic_return=3.0

global_step=2968, episodic_return=0.0

SPS: 60

global_step=3116, episodic_return=0.0

global_step=3160, episodic_return=4.0

global_step=3244, episodic_return=0.0

global_step=3320, episodic_return=3.0

global_step=3388, episodic_return=0.0

global_step=3512, episodic_return=0.0

global_step=3656, episodic_return=0.0

global_step=3704, episodic_return=3.0

global_step=3728, episodic_return=4.0

global_step=3812, episodic_return=0.0

global_step=3972, episodic_return=0.0

global_step=4000, episodic_return=4.0

global_step=4060, episodic_return=2.0

SPS: 61

global_step=4120, episodic_return=0.0

global_step=4260, episodic_return=0.0

global_step=4400, episodic_return=0.0

global_step=4440, episodic_return=3.0

global_step=4528, episodic_return=0.0

global_step=4676, episodic_return=0.0

global_step=4724, episodic_return=6.0

global_step=4760, episodic_return=3.0

global_step=4792, episodic_return=0.0

global_step=4820, episodic_return=3.0

global_step=4972, episodic_return=1.0

global_step=5108, episodic_return=0.0

SPS: 60

global_step=5180, episodic_return=3.0

global_step=5288, episodic_return=1.0

global_step=5400, episodic_return=0.0

global_step=5540, episodic_return=0.0

global_step=5564, episodic_return=3.0

global_step=5572, episodic_return=3.0

global_step=5676, episodic_return=0.0

global_step=5712, episodic_return=6.0

global_step=5816, episodic_return=0.0

global_step=5948, episodic_return=3.0

global_step=5976, episodic_return=0.0

global_step=6120, episodic_return=0.0

SPS: 60

global_step=6252, episodic_return=0.0

global_step=6328, episodic_return=3.0

global_step=6404, episodic_return=1.0

global_step=6412, episodic_return=4.0

global_step=6556, episodic_return=0.0

global_step=6676, episodic_return=4.0

global_step=6688, episodic_return=4.0

global_step=6700, episodic_return=0.0

global_step=6836, episodic_return=0.0

global_step=6964, episodic_return=0.0

global_step=7068, episodic_return=3.0

global_step=7136, episodic_return=1.0
.....................
@nmitra28
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#11 DomainRandomization with seed with sample_weights
space = TaskSpace(gym.spaces.Discrete(4), ["a", "b", "c","d"])
c = DomainRandomization(task_space = space, seed = seed, sample_weights = [0.6,0.2,0.1,0.1])
if seed_test(c = c) :
print("DomainRandomization with seed with sample weights! SUCCESSFUL")
#12: DomainRandomization without seed
c = DomainRandomization(task_space = space, sample_weights = [0.3,0.2,0.4,0.1])
if no_seed_test(c = c) :
print("DomainRandomization without seed with sample weights! SUCCESSFUL")

[0]

[0]

[0]

[0]

[0]

DomainRandomization with seed with sample weights! SUCCESSFUL

[3]

[0]

[2]

[2]

[2]

DomainRandomization without seed with sample weights! SUCCESSFUL
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