issues for ruunning train.py with TD3

[faker52]

nohup: ignoring input
[robosuite WARNING] No private macro file found! (init.py:7)
[robosuite WARNING] It is recommended to use a private macro file (init.py:8)
[robosuite WARNING] To setup, run: python /home/qxx/anaconda3/envs/robosuite/lib/python3.8/site-packages/robosuite/scripts/setup_macros.py (init.py:9)
No personal conf_private.py found.
doodad not detected

------------- Running TD3 --------------
Params:
variant: scripts/variantTD3Pnp.json

2023-11-07 18:43:56.892015 CST | Variant:
2023-11-07 18:43:56.892349 CST | {
"algorithm": "TD3",
"algorithm_kwargs": {
"batch_size": 128,
"eval_max_path_length": 500,
"expl_max_path_length": 500,
"min_num_steps_before_training": 3300,
"num_epochs": 2000,
"num_eval_steps_per_epoch": 2500,
"num_expl_steps_per_train_loop": 2500,
"num_trains_per_train_loop": 1000
},
"eval_environment_kwargs": {
"control_freq": 20,
"controller": "OSC_POSE",
"env_name": "Lift",
"hard_reset": false,
"horizon": 500,
"ignore_done": true,
"reward_scale": 1.0,
"robots": "['Panda']"
},
"expl_environment_kwargs": {
"control_freq": 20,
"controller": "OSC_POSE",
"env_name": "PickPlaceCan",
"hard_reset": false,
"horizon": 500,
"ignore_done": true,
"reward_scale": 1.0,
"robots": "['Panda']"
},
"policy_kwargs": {
"hidden_sizes": [
256,
256
]
},
"qf_kwargs": {
"hidden_sizes": [
256,
256
]
},
"replay_buffer_size": 1000000,
"seed": 17,
"trainer_kwargs": {
"discount": 0.99,
"reward_scale": 1.0
},
"version": "normal"
}
/home/qxx/anaconda3/envs/robosuite/lib/python3.8/site-packages/gym/spaces/box.py:127: UserWarning: �[33mWARN: Box bound precision lowered by casting to float32�[0m
logger.warn(f"Box bound precision lowered by casting to {self.dtype}")
Traceback (most recent call last):
File "scripts/train.py", line 131, in
run_experiment()
File "scripts/train.py", line 104, in run_experiment
experiment(variant, agent=args.agent)
File "/data1/qxx/RL/Change-maple/bseline/robosuite-benchmark/util/rlkit_utils.py", line 163, in experiment
algorithm.train()
File "/data1/qxx/RL/Change-maple/bseline/robosuite-benchmark/util/rlkit_custom.py", line 46, in train
self._train()
File "/data1/qxx/RL/Change-maple/bseline/robosuite-benchmark/util/rlkit_custom.py", line 213, in _train
self.eval_data_collector.collect_new_paths(
File "/data1/qxx/RL/Change-maple/bseline/rlkit/rlkit/samplers/data_collector/path_collector.py", line 42, in collect_new_paths
path = rollout(
File "/data1/qxx/RL/Change-maple/bseline/rlkit/rlkit/samplers/rollout_functions.py", line 112, in rollout
a, agent_info = agent.get_action(o)
File "/data1/qxx/RL/Change-maple/bseline/rlkit/rlkit/torch/networks.py", line 111, in get_action
actions = self.get_actions(obs_np[None])
File "/data1/qxx/RL/Change-maple/bseline/rlkit/rlkit/torch/networks.py", line 115, in get_actions
return eval_np(self, obs)
File "/data1/qxx/RL/Change-maple/bseline/rlkit/rlkit/torch/core.py", line 18, in eval_np
outputs = module(*torch_args, **torch_kwargs)
File "/home/qxx/anaconda3/envs/robosuite/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/qxx/anaconda3/envs/robosuite/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/data1/qxx/RL/Change-maple/bseline/rlkit/rlkit/torch/networks.py", line 108, in forward
return super().forward(obs, **kwargs)
File "/data1/qxx/RL/Change-maple/bseline/rlkit/rlkit/torch/networks.py", line 69, in forward
h = fc(h)
File "/home/qxx/anaconda3/envs/robosuite/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/qxx/anaconda3/envs/robosuite/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/home/qxx/anaconda3/envs/robosuite/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 114, in forward
return F.linear(input, self.weight, self.bias)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (1x42 and 46x256)

[amandlek]

We need more information to reproduce the issue. Can you give us the precise training command, config being used, and information on the dataset?