feature(zjow): add Implicit Q-Learning #821
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PaParaZz1
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PaParaZz1
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Jul 30, 2024
| ), | ||
| collect=dict(data_type='d4rl', ), | ||
| eval=dict(evaluator=dict(eval_freq=5000, )), | ||
| other=dict(replay_buffer=dict(replay_buffer_size=2000000, ), ), |
| config = Path(__file__).absolute().parent.parent / 'config' / args.config | ||
| config = read_config(str(config)) | ||
| config[0].exp_name = config[0].exp_name.replace('0', str(args.seed)) | ||
| serial_pipeline_offline(config, seed=args.seed) |
| @@ -114,6 +114,38 @@ def __init__(self, cfg: dict) -> None: | |||
| except (KeyError, AttributeError): | |||
| # do not normalize | |||
| pass | |||
| if hasattr(cfg.env, "reward_norm"): | |||
| if cfg.env.reward_norm == "normalize": | |||
| dataset['rewards'] = (dataset['rewards'] - dataset['rewards'].mean()) / dataset['rewards'].std() | |||
| @@ -0,0 +1,654 @@ | |||
| from typing import List, Dict, Any, Tuple, Union | |||
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add this policy into the table in readme
| # (str type) action_space: Use reparameterization trick for continous action | ||
| action_space='reparameterization', | ||
| # (int) Hidden size for actor network head. | ||
| actor_head_hidden_size=512, |
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add more comments for each arguments
| 'policy_grad_norm': policy_grad_norm, | ||
| } | ||
|
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| def _get_policy_actions(self, data: Dict, num_actions: int = 10, epsilon: float = 1e-6) -> List: |
| # 9. update policy network | ||
| self._optimizer_policy.zero_grad() | ||
| policy_loss.backward() | ||
| policy_grad_norm = torch.nn.utils.clip_grad_norm_(self._model.actor.parameters(), 1) |
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enable the argument can be set in the optimizer
| transforms=[TanhTransform(cache_size=1), | ||
| AffineTransform(loc=0.0, scale=1.05)] | ||
| ) | ||
| next_action = next_obs_dist.rsample() |
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why rsample rather than sample here
| log_prob = dist.log_prob(action) | ||
|
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| eval_data = {'obs': obs, 'action': action} | ||
| new_value = self._learn_model.forward(eval_data, mode='compute_critic') |
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maybe you can use with torch.no_grad() here
| with torch.no_grad(): | ||
| (mu, sigma) = self._collect_model.forward(data, mode='compute_actor')['logit'] | ||
| dist = Independent(Normal(mu, sigma), 1) | ||
| action = torch.tanh(dist.rsample()) |
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for offline RL algorithm, you may opt to leave the methods related to collect with empty
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Add Implicit Q-Learning (IQL) algorithm.