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428516056a
* Policy-classes cleanup and torch/tf unification. - Make Policy abstract. - Add `action_dist` to call to `extra_action_out_fn` (necessary for PPO torch). - Move some methods and vars to base Policy (from TFPolicy): num_state_tensors, ACTION_PROB, ACTION_LOGP and some more. * Fix `clip_action` import from Policy (should probably be moved into utils altogether). * - Move `is_recurrent()` and `num_state_tensors()` into TFPolicy (from DynamicTFPolicy). - Add config to all Policy c'tor calls (as 3rd arg after obs and action spaces). * Add `config` to c'tor call to TFPolicy. * Add missing `config` to c'tor call to TFPolicy in marvil_policy.py. * Fix test_rollout_worker.py::MockPolicy and BadPolicy classes (Policy base class is now abstract). * Fix LINT errors in Policy classes. * Implement StatefulPolicy abstract methods in test cases: test_multi_agent_env.py. * policy.py LINT errors. * Create a simple TestPolicy to sub-class from when testing Policies (reduces code in some test cases). * policy.py - Remove abstractmethod from `apply_gradients` and `compute_gradients` (these are not required iff `learn_on_batch` implemented). - Fix docstring of `num_state_tensors`. * Make QMIX torch Policy a child of TorchPolicy (instead of Policy). * QMixPolicy add empty implementations of abstract Policy methods. * Store Policy's config in self.config in base Policy c'tor. * - Make only compute_actions in base Policy's an abstractmethod and provide pass implementation to all other methods if not defined. - Fix state_batches=None (most Policies don't have internal states). * Cartpole tf learning. * Cartpole tf AND torch learning (in ~ same ts). * Cartpole tf AND torch learning (in ~ same ts). 2 * Cartpole tf (torch syntax-broken) learning (in ~ same ts). 3 * Cartpole tf AND torch learning (in ~ same ts). 4 * Cartpole tf AND torch learning (in ~ same ts). 5 * Cartpole tf AND torch learning (in ~ same ts). 6 * Cartpole tf AND torch learning (in ~ same ts). Pendulum tf learning. * WIP. * WIP. * SAC torch learning Pendulum. * WIP. * SAC torch and tf learning Pendulum and Cartpole after cleanup. * WIP. * LINT. * LINT. * SAC: Move policy.target_model to policy.device as well. * Fixes and cleanup. * Fix data-format of tf keras Conv2d layers (broken for some tf-versions which have data_format="channels_first" as default). * Fixes and LINT. * Fixes and LINT. * Fix and LINT. * WIP. * Test fixes and LINT. * Fixes and LINT. Co-authored-by: Sven Mika <sven@Svens-MacBook-Pro.local>
86 lines
2.8 KiB
Python
86 lines
2.8 KiB
Python
import ray
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from ray.rllib.evaluation.postprocessing import compute_advantages, \
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Postprocessing
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from ray.rllib.policy.sample_batch import SampleBatch
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from ray.rllib.policy.torch_policy_template import build_torch_policy
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from ray.rllib.utils.framework import try_import_torch
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torch, nn = try_import_torch()
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def actor_critic_loss(policy, model, dist_class, train_batch):
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logits, _ = model.from_batch(train_batch)
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values = model.value_function()
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dist = dist_class(logits, model)
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log_probs = dist.logp(train_batch[SampleBatch.ACTIONS])
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policy.entropy = dist.entropy().mean()
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policy.pi_err = -train_batch[Postprocessing.ADVANTAGES].dot(
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log_probs.reshape(-1))
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policy.value_err = nn.functional.mse_loss(
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values.reshape(-1), train_batch[Postprocessing.VALUE_TARGETS])
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overall_err = sum([
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policy.pi_err,
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policy.config["vf_loss_coeff"] * policy.value_err,
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-policy.config["entropy_coeff"] * policy.entropy,
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])
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return overall_err
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def loss_and_entropy_stats(policy, train_batch):
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return {
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"policy_entropy": policy.entropy.item(),
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"policy_loss": policy.pi_err.item(),
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"vf_loss": policy.value_err.item(),
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}
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def add_advantages(policy,
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sample_batch,
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other_agent_batches=None,
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episode=None):
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completed = sample_batch[SampleBatch.DONES][-1]
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if completed:
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last_r = 0.0
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else:
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last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1])
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return compute_advantages(
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sample_batch, last_r, policy.config["gamma"], policy.config["lambda"],
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policy.config["use_gae"], policy.config["use_critic"])
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def model_value_predictions(policy, input_dict, state_batches, model,
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action_dist):
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return {SampleBatch.VF_PREDS: model.value_function()}
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def apply_grad_clipping(policy, optimizer, loss):
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info = {}
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if policy.config["grad_clip"]:
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for param_group in optimizer.param_groups:
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info["grad_gnorm"] = nn.utils.clip_grad_norm_(
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param_group["params"], policy.config["grad_clip"])
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return info
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def torch_optimizer(policy, config):
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return torch.optim.Adam(policy.model.parameters(), lr=config["lr"])
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class ValueNetworkMixin:
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def _value(self, obs):
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_ = self.model({"obs": torch.Tensor([obs]).to(self.device)}, [], [1])
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return self.model.value_function()[0]
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A3CTorchPolicy = build_torch_policy(
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name="A3CTorchPolicy",
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get_default_config=lambda: ray.rllib.agents.a3c.a3c.DEFAULT_CONFIG,
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loss_fn=actor_critic_loss,
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stats_fn=loss_and_entropy_stats,
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postprocess_fn=add_advantages,
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extra_action_out_fn=model_value_predictions,
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extra_grad_process_fn=apply_grad_clipping,
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optimizer_fn=torch_optimizer,
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mixins=[ValueNetworkMixin])
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