When I run the example. I have an RuntimeError: mat1 and mat2 shapes cannot be multiplied (18x1 and 18x256)

[lk1983823]

When I run the command
python examples/train_task.py --algo_name=mopo --exp_name=halfcheetah --task HalfCheetah-v3 --task_data_type low --task_train_num 2
It shows :

File "examples/train_task.py", line 19, in <module>
 fire.Fire(run_algo)
 File "/home/lksgcc/.pyenv/versions/anaconda3-5.0.1/envs/mujoco_py/lib/python3.8/site-packages/fire/core.py", line 141, in Fire
 component_trace = _Fire(component, args, parsed_flag_args, context, name)
 File "/home/lksgcc/.pyenv/versions/anaconda3-5.0.1/envs/mujoco_py/lib/python3.8/site-packages/fire/core.py", line 466, in _Fire
 component, remaining_args = _CallAndUpdateTrace(
 File "/home/lksgcc/.pyenv/versions/anaconda3-5.0.1/envs/mujoco_py/lib/python3.8/site-packages/fire/core.py", line 681, in _CallAndUpdateTrace
 component = fn(*varargs, **kwargs)
 File "examples/train_task.py", line 16, in run_algo
 algo_trainer.train(train_buffer, val_buffer, callback_fn=callback)
 File "/media/lksgcc/new_disk/lk_git/3_Reinforcement_Learning/3_2_Offline_Learning/OfflineRL/offlinerl/algo/modelbase/mopo.py", line 94, in train
 self.train_policy(train_buffer, val_buffer, self.transition, callback_fn)
 File "/media/lksgcc/new_disk/lk_git/3_Reinforcement_Learning/3_2_Offline_Learning/OfflineRL/offlinerl/algo/modelbase/mopo.py", line 206, in train_policy
 res = callback_fn(self.get_policy())
 File "/media/lksgcc/new_disk/lk_git/3_Reinforcement_Learning/3_2_Offline_Learning/OfflineRL/offlinerl/evaluation/__init__.py", line 80, in __call__
 eval_res.update(test_on_real_env(policy, self.env, number_of_runs=self.number_of_runs))
 File "/media/lksgcc/new_disk/lk_git/3_Reinforcement_Learning/3_2_Offline_Learning/OfflineRL/offlinerl/evaluation/neorl.py", line 54, in test_on_real_env
 results = [test_one_trail_sp_local(env, policy) for _ in range(number_of_runs)]
 File "/media/lksgcc/new_disk/lk_git/3_Reinforcement_Learning/3_2_Offline_Learning/OfflineRL/offlinerl/evaluation/neorl.py", line 54, in <listcomp>
 results = [test_one_trail_sp_local(env, policy) for _ in range(number_of_runs)]
 File "/media/lksgcc/new_disk/lk_git/3_Reinforcement_Learning/3_2_Offline_Learning/OfflineRL/offlinerl/evaluation/neorl.py", line 39, in test_one_trail_sp_local
 action = policy.get_action(state).reshape(-1, act_dim)
 File "/media/lksgcc/new_disk/lk_git/3_Reinforcement_Learning/3_2_Offline_Learning/OfflineRL/offlinerl/utils/net/common.py", line 33, in get_action
 act = to_array_as(self.policy_infer(obs_tensor), obs)
 File "/media/lksgcc/new_disk/lk_git/3_Reinforcement_Learning/3_2_Offline_Learning/OfflineRL/offlinerl/utils/net/tanhpolicy.py", line 164, in policy_infer
 return self(obs).mode
 File "/home/lksgcc/.pyenv/versions/anaconda3-5.0.1/envs/mujoco_py/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
 return forward_call(*input, **kwargs)
 File "/media/lksgcc/new_disk/lk_git/3_Reinforcement_Learning/3_2_Offline_Learning/OfflineRL/offlinerl/utils/net/tanhpolicy.py", line 147, in forward
 logits, h = self.preprocess(obs, state)
 File "/home/lksgcc/.pyenv/versions/anaconda3-5.0.1/envs/mujoco_py/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
 return forward_call(*input, **kwargs)
 File "/media/lksgcc/new_disk/lk_git/3_Reinforcement_Learning/3_2_Offline_Learning/OfflineRL/offlinerl/utils/net/common.py", line 113, in forward
 logits = self.model(s)
 File "/home/lksgcc/.pyenv/versions/anaconda3-5.0.1/envs/mujoco_py/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
 return forward_call(*input, **kwargs)
 File "/home/lksgcc/.pyenv/versions/anaconda3-5.0.1/envs/mujoco_py/lib/python3.8/site-packages/torch/nn/modules/container.py", line 141, in forward
 input = module(input)
 File "/home/lksgcc/.pyenv/versions/anaconda3-5.0.1/envs/mujoco_py/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
 return forward_call(*input, **kwargs)
 File "/home/lksgcc/.pyenv/versions/anaconda3-5.0.1/envs/mujoco_py/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 103, in forward
 return F.linear(input, self.weight, self.bias)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (18x1 and 18x256)

Other algos also show the same error. Thanks for solving this problem!

[linhlpv]

Hi @lk1983823, I have faced with ur bug and I think what happens in here is that the shape of the state is not in the right way. State must has its shape like [batch_size, num_feats]. So I change a little bit in the file offlinerl/evaluation/neorl.py, from action = policy.get_action(state).reshape(-1, act_dim) to if len(state.shape) == 1: state = state.reshape(-1, state.shape[0]) action = policy.get_action(state).reshape(-1, act_dim) if len(action.shape) == 1: action = action.reshape(-1, action.shape[0])
Hope it can help.