mirror of
https://github.com/vale981/ray
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[RLlib] Dreamer Policy sub-classing schema. (#25585)
This commit is contained in:
Artur Niederfahrenhorst
GitHub
parent
65d7a610ab
commit
7495e9c89c
2 changed files with 231 additions and 248 deletions
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@ -1,19 +1,27 @@
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import logging
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from typing import (
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List,
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Tuple,
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Union,
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)
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import logging
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import ray
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import numpy as np
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from typing import Dict, Optional
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import ray
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from ray.rllib.algorithms.dreamer.utils import FreezeParameters
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from ray.rllib.evaluation.episode import Episode
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from ray.rllib.models.catalog import ModelCatalog
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from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
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from ray.rllib.policy.policy import Policy
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from ray.rllib.policy.policy_template import build_policy_class
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from ray.rllib.policy.sample_batch import SampleBatch
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from ray.rllib.utils.framework import try_import_torch
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from ray.rllib.utils.torch_utils import apply_grad_clipping
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from ray.rllib.utils.typing import AgentID, TensorType
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from ray.rllib.utils.annotations import override
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from ray.rllib.models.action_dist import ActionDistribution
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from ray.rllib.models.modelv2 import ModelV2
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from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
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torch, nn = try_import_torch()
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if torch:
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@ -22,126 +30,239 @@ if torch:
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logger = logging.getLogger(__name__)
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# This is the computation graph for workers (inner adaptation steps)
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def compute_dreamer_loss(
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obs: TensorType,
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action: TensorType,
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reward: TensorType,
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model: TorchModelV2,
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imagine_horizon: int,
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gamma: float = 0.99,
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lambda_: float = 0.95,
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kl_coeff: float = 1.0,
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free_nats: float = 3.0,
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log: bool = False,
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):
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"""Constructs loss for the Dreamer objective.
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class DreamerTorchPolicy(TorchPolicyV2):
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def __init__(self, observation_space, action_space, config):
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Args:
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obs: Observations (o_t).
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action: Actions (a_(t-1)).
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reward: Rewards (r_(t-1)).
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model: DreamerModel, encompassing all other models.
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imagine_horizon: Imagine horizon for actor and critic loss.
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gamma: Discount factor gamma.
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lambda_: Lambda, like in GAE.
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kl_coeff: KL Coefficient for Divergence loss in model loss.
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free_nats: Threshold for minimum divergence in model loss.
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log: If log, generate gifs.
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"""
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encoder_weights = list(model.encoder.parameters())
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decoder_weights = list(model.decoder.parameters())
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reward_weights = list(model.reward.parameters())
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dynamics_weights = list(model.dynamics.parameters())
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critic_weights = list(model.value.parameters())
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model_weights = list(
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encoder_weights + decoder_weights + reward_weights + dynamics_weights
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)
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config = dict(ray.rllib.algorithms.dreamer.DreamerConfig().to_dict(), **config)
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
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TorchPolicyV2.__init__(
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self,
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observation_space,
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action_space,
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config,
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max_seq_len=config["model"]["max_seq_len"],
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)
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# PlaNET Model Loss
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latent = model.encoder(obs)
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post, prior = model.dynamics.observe(latent, action)
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features = model.dynamics.get_feature(post)
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image_pred = model.decoder(features)
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reward_pred = model.reward(features)
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image_loss = -torch.mean(image_pred.log_prob(obs))
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reward_loss = -torch.mean(reward_pred.log_prob(reward))
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prior_dist = model.dynamics.get_dist(prior[0], prior[1])
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post_dist = model.dynamics.get_dist(post[0], post[1])
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div = torch.mean(
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torch.distributions.kl_divergence(post_dist, prior_dist).sum(dim=2)
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)
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div = torch.clamp(div, min=free_nats)
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model_loss = kl_coeff * div + reward_loss + image_loss
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# TODO: Don't require users to call this manually.
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self._initialize_loss_from_dummy_batch()
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# Actor Loss
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# [imagine_horizon, batch_length*batch_size, feature_size]
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with torch.no_grad():
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actor_states = [v.detach() for v in post]
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with FreezeParameters(model_weights):
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imag_feat = model.imagine_ahead(actor_states, imagine_horizon)
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with FreezeParameters(model_weights + critic_weights):
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reward = model.reward(imag_feat).mean
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value = model.value(imag_feat).mean
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pcont = gamma * torch.ones_like(reward)
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returns = lambda_return(reward[:-1], value[:-1], pcont[:-1], value[-1], lambda_)
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discount_shape = pcont[:1].size()
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discount = torch.cumprod(
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torch.cat([torch.ones(*discount_shape).to(device), pcont[:-2]], dim=0), dim=0
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)
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actor_loss = -torch.mean(discount * returns)
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@override(TorchPolicyV2)
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def loss(
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self, model: ModelV2, dist_class: ActionDistribution, train_batch: SampleBatch
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) -> Union[TensorType, List[TensorType]]:
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log_gif = False
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if "log_gif" in train_batch:
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log_gif = True
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# Critic Loss
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with torch.no_grad():
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val_feat = imag_feat.detach()[:-1]
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target = returns.detach()
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val_discount = discount.detach()
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val_pred = model.value(val_feat)
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critic_loss = -torch.mean(val_discount * val_pred.log_prob(target))
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# This is the computation graph for workers (inner adaptation steps)
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encoder_weights = list(self.model.encoder.parameters())
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decoder_weights = list(self.model.decoder.parameters())
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reward_weights = list(self.model.reward.parameters())
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dynamics_weights = list(self.model.dynamics.parameters())
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critic_weights = list(self.model.value.parameters())
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model_weights = list(
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encoder_weights + decoder_weights + reward_weights + dynamics_weights
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)
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device = (
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torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
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)
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# Logging purposes
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prior_ent = torch.mean(prior_dist.entropy())
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post_ent = torch.mean(post_dist.entropy())
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# PlaNET Model Loss
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latent = self.model.encoder(train_batch["obs"])
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post, prior = self.model.dynamics.observe(latent, train_batch["actions"])
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features = self.model.dynamics.get_feature(post)
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image_pred = self.model.decoder(features)
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reward_pred = self.model.reward(features)
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image_loss = -torch.mean(image_pred.log_prob(train_batch["obs"]))
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reward_loss = -torch.mean(reward_pred.log_prob(train_batch["rewards"]))
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prior_dist = self.model.dynamics.get_dist(prior[0], prior[1])
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post_dist = self.model.dynamics.get_dist(post[0], post[1])
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div = torch.mean(
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torch.distributions.kl_divergence(post_dist, prior_dist).sum(dim=2)
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)
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div = torch.clamp(div, min=(self.config["free_nats"]))
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model_loss = self.config["kl_coeff"] * div + reward_loss + image_loss
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log_gif = None
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if log:
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log_gif = log_summary(obs, action, latent, image_pred, model)
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# Actor Loss
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# [imagine_horizon, batch_length*batch_size, feature_size]
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with torch.no_grad():
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actor_states = [v.detach() for v in post]
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with FreezeParameters(model_weights):
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imag_feat = self.model.imagine_ahead(
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actor_states, self.config["imagine_horizon"]
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)
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with FreezeParameters(model_weights + critic_weights):
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reward = self.model.reward(imag_feat).mean
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value = self.model.value(imag_feat).mean
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pcont = self.config["gamma"] * torch.ones_like(reward)
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return_dict = {
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"model_loss": model_loss,
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"reward_loss": reward_loss,
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"image_loss": image_loss,
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"divergence": div,
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"actor_loss": actor_loss,
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"critic_loss": critic_loss,
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"prior_ent": prior_ent,
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"post_ent": post_ent,
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}
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# Similar to GAE-Lambda, calculate value targets
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next_values = torch.cat([value[:-1][1:], value[-1][None]], dim=0)
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inputs = reward[:-1] + pcont[:-1] * next_values * (1 - self.config["lambda"])
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if log_gif is not None:
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return_dict["log_gif"] = log_gif
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return return_dict
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def agg_fn(x, y):
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return y[0] + y[1] * self.config["lambda"] * x
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last = value[-1]
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returns = []
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for i in reversed(range(len(inputs))):
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last = agg_fn(last, [inputs[i], pcont[:-1][i]])
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returns.append(last)
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# Similar to GAE-Lambda, calculate value targets
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def lambda_return(reward, value, pcont, bootstrap, lambda_):
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def agg_fn(x, y):
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return y[0] + y[1] * lambda_ * x
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returns = list(reversed(returns))
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returns = torch.stack(returns, dim=0)
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discount_shape = pcont[:1].size()
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discount = torch.cumprod(
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torch.cat([torch.ones(*discount_shape).to(device), pcont[:-2]], dim=0),
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dim=0,
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)
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actor_loss = -torch.mean(discount * returns)
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next_values = torch.cat([value[1:], bootstrap[None]], dim=0)
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inputs = reward + pcont * next_values * (1 - lambda_)
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# Critic Loss
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with torch.no_grad():
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val_feat = imag_feat.detach()[:-1]
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target = returns.detach()
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val_discount = discount.detach()
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val_pred = self.model.value(val_feat)
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critic_loss = -torch.mean(val_discount * val_pred.log_prob(target))
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last = bootstrap
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returns = []
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for i in reversed(range(len(inputs))):
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last = agg_fn(last, [inputs[i], pcont[i]])
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returns.append(last)
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# Logging purposes
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prior_ent = torch.mean(prior_dist.entropy())
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post_ent = torch.mean(post_dist.entropy())
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gif = None
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if log_gif:
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gif = log_summary(
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train_batch["obs"],
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train_batch["actions"],
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latent,
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image_pred,
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self.model,
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)
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return_dict = {
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"model_loss": model_loss,
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"reward_loss": reward_loss,
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"image_loss": image_loss,
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"divergence": div,
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"actor_loss": actor_loss,
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"critic_loss": critic_loss,
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"prior_ent": prior_ent,
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"post_ent": post_ent,
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}
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if gif is not None:
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return_dict["log_gif"] = gif
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self.stats_dict = return_dict
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returns = list(reversed(returns))
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returns = torch.stack(returns, dim=0)
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return returns
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loss_dict = self.stats_dict
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return (
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loss_dict["model_loss"],
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loss_dict["actor_loss"],
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loss_dict["critic_loss"],
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)
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@override(TorchPolicyV2)
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def postprocess_trajectory(
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self,
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sample_batch: SampleBatch,
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other_agent_batches: Optional[
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Dict[AgentID, Tuple["Policy", SampleBatch]]
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] = None,
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episode: Optional["Episode"] = None,
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) -> SampleBatch:
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"""Batch format should be in the form of (s_t, a_(t-1), r_(t-1))
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When t=0, the resetted obs is paired with action and reward of 0.
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"""
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obs = sample_batch[SampleBatch.OBS]
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new_obs = sample_batch[SampleBatch.NEXT_OBS]
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action = sample_batch[SampleBatch.ACTIONS]
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reward = sample_batch[SampleBatch.REWARDS]
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eps_ids = sample_batch[SampleBatch.EPS_ID]
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act_shape = action.shape
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act_reset = np.array([0.0] * act_shape[-1])[None]
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rew_reset = np.array(0.0)[None]
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obs_end = np.array(new_obs[act_shape[0] - 1])[None]
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batch_obs = np.concatenate([obs, obs_end], axis=0)
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batch_action = np.concatenate([act_reset, action], axis=0)
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batch_rew = np.concatenate([rew_reset, reward], axis=0)
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batch_eps_ids = np.concatenate([eps_ids, eps_ids[-1:]], axis=0)
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new_batch = {
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SampleBatch.OBS: batch_obs,
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SampleBatch.REWARDS: batch_rew,
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SampleBatch.ACTIONS: batch_action,
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SampleBatch.EPS_ID: batch_eps_ids,
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}
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return SampleBatch(new_batch)
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def stats_fn(self, train_batch):
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return self.stats_dict
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@override(TorchPolicyV2)
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def optimizer(self):
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model = self.model
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encoder_weights = list(model.encoder.parameters())
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decoder_weights = list(model.decoder.parameters())
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reward_weights = list(model.reward.parameters())
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dynamics_weights = list(model.dynamics.parameters())
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actor_weights = list(model.actor.parameters())
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critic_weights = list(model.value.parameters())
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model_opt = torch.optim.Adam(
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encoder_weights + decoder_weights + reward_weights + dynamics_weights,
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lr=self.config["td_model_lr"],
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)
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actor_opt = torch.optim.Adam(actor_weights, lr=self.config["actor_lr"])
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critic_opt = torch.optim.Adam(critic_weights, lr=self.config["critic_lr"])
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return (model_opt, actor_opt, critic_opt)
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def action_sampler_fn(policy, model, obs_batch, state_batches, explore, timestep):
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"""Action sampler function has two phases. During the prefill phase,
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actions are sampled uniformly [-1, 1]. During training phase, actions
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are evaluated through DreamerPolicy and an additive gaussian is added
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to incentivize exploration.
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"""
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obs = obs_batch["obs"]
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# Custom Exploration
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if timestep <= policy.config["prefill_timesteps"]:
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logp = None
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# Random action in space [-1.0, 1.0]
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action = 2.0 * torch.rand(1, model.action_space.shape[0]) - 1.0
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state_batches = model.get_initial_state()
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else:
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# Weird RLlib Handling, this happens when env rests
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if len(state_batches[0].size()) == 3:
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# Very hacky, but works on all envs
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state_batches = model.get_initial_state()
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action, logp, state_batches = model.policy(obs, state_batches, explore)
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action = td.Normal(action, policy.config["explore_noise"]).sample()
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action = torch.clamp(action, min=-1.0, max=1.0)
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policy.global_timestep += policy.config["action_repeat"]
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return action, logp, state_batches
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def make_model(self):
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model = ModelCatalog.get_model_v2(
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self.observation_space,
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self.action_space,
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1,
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self.config["dreamer_model"],
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name="DreamerModel",
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framework="torch",
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)
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self.model_variables = model.variables()
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return model
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def extra_grad_process(
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self, optimizer: "torch.optim.Optimizer", loss: TensorType
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) -> Dict[str, TensorType]:
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return apply_grad_clipping(self, optimizer, loss)
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# Creates gif
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@ -156,140 +277,3 @@ def log_summary(obs, action, embed, image_pred, model):
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mod = torch.cat([recon[:, :5] + 0.5, openl + 0.5], 1)
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error = (mod - truth + 1.0) / 2.0
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return torch.cat([truth, mod, error], 3)
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def dreamer_loss(policy, model, dist_class, train_batch):
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log_gif = False
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if "log_gif" in train_batch:
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log_gif = True
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policy.stats_dict = compute_dreamer_loss(
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train_batch["obs"],
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train_batch["actions"],
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train_batch["rewards"],
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policy.model,
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policy.config["imagine_horizon"],
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policy.config["gamma"],
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policy.config["lambda"],
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policy.config["kl_coeff"],
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policy.config["free_nats"],
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log_gif,
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)
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loss_dict = policy.stats_dict
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return (loss_dict["model_loss"], loss_dict["actor_loss"], loss_dict["critic_loss"])
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def build_dreamer_model(policy, obs_space, action_space, config):
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model = ModelCatalog.get_model_v2(
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obs_space,
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action_space,
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1,
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config["dreamer_model"],
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name="DreamerModel",
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framework="torch",
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)
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policy.model_variables = model.variables()
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return model
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def action_sampler_fn(policy, model, input_dict, state, explore, timestep):
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"""Action sampler function has two phases. During the prefill phase,
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actions are sampled uniformly [-1, 1]. During training phase, actions
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are evaluated through DreamerPolicy and an additive gaussian is added
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to incentivize exploration.
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"""
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obs = input_dict["obs"]
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# Custom Exploration
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if timestep <= policy.config["prefill_timesteps"]:
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logp = None
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# Random action in space [-1.0, 1.0]
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action = 2.0 * torch.rand(1, model.action_space.shape[0]) - 1.0
|
||||
state = model.get_initial_state()
|
||||
else:
|
||||
# Weird RLlib Handling, this happens when env rests
|
||||
if len(state[0].size()) == 3:
|
||||
# Very hacky, but works on all envs
|
||||
state = model.get_initial_state()
|
||||
action, logp, state = model.policy(obs, state, explore)
|
||||
action = td.Normal(action, policy.config["explore_noise"]).sample()
|
||||
action = torch.clamp(action, min=-1.0, max=1.0)
|
||||
|
||||
policy.global_timestep += policy.config["action_repeat"]
|
||||
|
||||
return action, logp, state
|
||||
|
||||
|
||||
def dreamer_stats(policy, train_batch):
|
||||
return policy.stats_dict
|
||||
|
||||
|
||||
def dreamer_optimizer_fn(policy, config):
|
||||
model = policy.model
|
||||
encoder_weights = list(model.encoder.parameters())
|
||||
decoder_weights = list(model.decoder.parameters())
|
||||
reward_weights = list(model.reward.parameters())
|
||||
dynamics_weights = list(model.dynamics.parameters())
|
||||
actor_weights = list(model.actor.parameters())
|
||||
critic_weights = list(model.value.parameters())
|
||||
model_opt = torch.optim.Adam(
|
||||
encoder_weights + decoder_weights + reward_weights + dynamics_weights,
|
||||
lr=config["td_model_lr"],
|
||||
)
|
||||
actor_opt = torch.optim.Adam(actor_weights, lr=config["actor_lr"])
|
||||
critic_opt = torch.optim.Adam(critic_weights, lr=config["critic_lr"])
|
||||
|
||||
return (model_opt, actor_opt, critic_opt)
|
||||
|
||||
|
||||
def preprocess_episode(
|
||||
policy: Policy,
|
||||
sample_batch: SampleBatch,
|
||||
other_agent_batches: Optional[Dict[AgentID, SampleBatch]] = None,
|
||||
episode: Optional[Episode] = None,
|
||||
) -> SampleBatch:
|
||||
"""Batch format should be in the form of (s_t, a_(t-1), r_(t-1))
|
||||
When t=0, the resetted obs is paired with action and reward of 0.
|
||||
"""
|
||||
obs = sample_batch[SampleBatch.OBS]
|
||||
new_obs = sample_batch[SampleBatch.NEXT_OBS]
|
||||
action = sample_batch[SampleBatch.ACTIONS]
|
||||
reward = sample_batch[SampleBatch.REWARDS]
|
||||
eps_ids = sample_batch[SampleBatch.EPS_ID]
|
||||
|
||||
act_shape = action.shape
|
||||
act_reset = np.array([0.0] * act_shape[-1])[None]
|
||||
rew_reset = np.array(0.0)[None]
|
||||
obs_end = np.array(new_obs[act_shape[0] - 1])[None]
|
||||
|
||||
batch_obs = np.concatenate([obs, obs_end], axis=0)
|
||||
batch_action = np.concatenate([act_reset, action], axis=0)
|
||||
batch_rew = np.concatenate([rew_reset, reward], axis=0)
|
||||
batch_eps_ids = np.concatenate([eps_ids, eps_ids[-1:]], axis=0)
|
||||
|
||||
new_batch = {
|
||||
SampleBatch.OBS: batch_obs,
|
||||
SampleBatch.REWARDS: batch_rew,
|
||||
SampleBatch.ACTIONS: batch_action,
|
||||
SampleBatch.EPS_ID: batch_eps_ids,
|
||||
}
|
||||
return SampleBatch(new_batch)
|
||||
|
||||
|
||||
DreamerTorchPolicy = build_policy_class(
|
||||
name="DreamerTorchPolicy",
|
||||
framework="torch",
|
||||
get_default_config=lambda: ray.rllib.algorithms.dreamer.dreamer.DEFAULT_CONFIG,
|
||||
action_sampler_fn=action_sampler_fn,
|
||||
postprocess_fn=preprocess_episode,
|
||||
loss_fn=dreamer_loss,
|
||||
stats_fn=dreamer_stats,
|
||||
make_model=build_dreamer_model,
|
||||
optimizer_fn=dreamer_optimizer_fn,
|
||||
extra_grad_process_fn=apply_grad_clipping,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1012,7 +1012,6 @@ class TorchPolicyV2(Policy):
|
|||
if is_overridden(self.action_sampler_fn):
|
||||
action_dist = dist_inputs = None
|
||||
actions, logp, state_out = self.action_sampler_fn(
|
||||
self,
|
||||
self.model,
|
||||
obs_batch=input_dict,
|
||||
state_batches=state_batches,
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue