436 lines
19 KiB
Python
436 lines
19 KiB
Python
from typing import Any, Dict, List, Optional, Tuple, Type, Union
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import gym
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import numpy as np
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import torch as th
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from torch.nn import functional as F
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from stable_baselines3.common.buffers import ReplayBuffer
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from stable_baselines3.common.noise import ActionNoise
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from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
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from stable_baselines3.common.policies import BasePolicy
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from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
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from stable_baselines3.common.utils import polyak_update
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from metastable_baselines.sac.policies import CnnPolicy, MlpPolicy, MultiInputPolicy, SACPolicy
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from ..misc.distTools import new_dist_like
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from metastable_projections.projections.base_projection_layer import BaseProjectionLayer
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from metastable_projections.projections.frob_projection_layer import FrobeniusProjectionLayer
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from metastable_projections.projections.w2_projection_layer import WassersteinProjectionLayer
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from metastable_projections.projections.kl_projection_layer import KLProjectionLayer
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from ..misc.rollout_buffer import GaussianRolloutCollectorAuxclass
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# CAP the standard deviation of the actor
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LOG_STD_MAX = 2
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LOG_STD_MIN = -20
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class SAC(OffPolicyAlgorithm):
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"""
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Trust Region Layers (TRL) based on SAC (Soft Actor Critic)
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This implementation is almost a 1:1-copy of the sb3-code for SAC.
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Only minor changes have been made to implement Differential Trust Region Layers
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Description from original SAC implementation:
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Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,
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This implementation borrows code from original implementation (https://github.com/haarnoja/sac)
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from OpenAI Spinning Up (https://github.com/openai/spinningup), from the softlearning repo
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(https://github.com/rail-berkeley/softlearning/)
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and from Stable Baselines (https://github.com/hill-a/stable-baselines)
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Paper: https://arxiv.org/abs/1801.01290
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Introduction to SAC: https://spinningup.openai.com/en/latest/algorithms/sac.html
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Note: we use double q target and not value target as discussed
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in https://github.com/hill-a/stable-baselines/issues/270
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:param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
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:param env: The environment to learn from (if registered in Gym, can be str)
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:param learning_rate: learning rate for adam optimizer,
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the same learning rate will be used for all networks (Q-Values, Actor and Value function)
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it can be a function of the current progress remaining (from 1 to 0)
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:param buffer_size: size of the replay buffer
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:param learning_starts: how many steps of the model to collect transitions for before learning starts
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:param batch_size: Minibatch size for each gradient update
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:param tau: the soft update coefficient ("Polyak update", between 0 and 1)
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:param gamma: the discount factor
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:param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit
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like ``(5, "step")`` or ``(2, "episode")``.
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:param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``)
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Set to ``-1`` means to do as many gradient steps as steps done in the environment
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during the rollout.
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:param action_noise: the action noise type (None by default), this can help
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for hard exploration problem. Cf common.noise for the different action noise type.
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:param replay_buffer_class: Replay buffer class to use (for instance ``HerReplayBuffer``).
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If ``None``, it will be automatically selected.
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:param replay_buffer_kwargs: Keyword arguments to pass to the replay buffer on creation.
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:param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
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at a cost of more complexity.
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See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
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:param ent_coef: Entropy regularization coefficient. (Equivalent to
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inverse of reward scale in the original SAC paper.) Controlling exploration/exploitation trade-off.
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Set it to 'auto' to learn it automatically (and 'auto_0.1' for using 0.1 as initial value)
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:param target_update_interval: update the target network every ``target_network_update_freq``
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gradient steps.
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:param target_entropy: target entropy when learning ``ent_coef`` (``ent_coef = 'auto'``)
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:param use_sde: Whether to use generalized State Dependent Exploration (gSDE)
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instead of action noise exploration (default: False)
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:param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
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Default: -1 (only sample at the beginning of the rollout)
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:param use_sde_at_warmup: Whether to use gSDE instead of uniform sampling
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during the warm up phase (before learning starts)
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:param create_eval_env: Whether to create a second environment that will be
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used for evaluating the agent periodically. (Only available when passing string for the environment)
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:param policy_kwargs: additional arguments to be passed to the policy on creation
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:param verbose: the verbosity level: 0 no output, 1 info, 2 debug
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:param seed: Seed for the pseudo random generators
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:param device: Device (cpu, cuda, ...) on which the code should be run.
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Setting it to auto, the code will be run on the GPU if possible.
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:param _init_setup_model: Whether or not to build the network at the creation of the instance
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"""
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policy_aliases: Dict[str, Type[BasePolicy]] = {
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"MlpPolicy": MlpPolicy,
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"CnnPolicy": CnnPolicy,
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"MultiInputPolicy": MultiInputPolicy,
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}
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def __init__(
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self,
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policy: Union[str, Type[SACPolicy]],
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env: Union[GymEnv, str],
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learning_rate: Union[float, Schedule] = 3e-4,
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buffer_size: int = 1_000_000, # 1e6
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learning_starts: int = 100,
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batch_size: int = 256,
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tau: float = 0.005,
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gamma: float = 0.99,
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train_freq: Union[int, Tuple[int, str]] = 1,
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gradient_steps: int = 1,
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action_noise: Optional[ActionNoise] = None,
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replay_buffer_class: Optional[ReplayBuffer] = None,
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replay_buffer_kwargs: Optional[Dict[str, Any]] = None,
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optimize_memory_usage: bool = False,
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ent_coef: Union[str, float] = "auto",
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target_update_interval: int = 1,
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target_entropy: Union[str, float] = "auto",
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use_sde: bool = False,
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sde_sample_freq: int = -1,
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use_sde_at_warmup: bool = False,
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tensorboard_log: Optional[str] = None,
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action_coef: float = 0.0,
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policy_kwargs: Optional[Dict[str, Any]] = None,
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verbose: int = 0,
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seed: Optional[int] = None,
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device: Union[th.device, str] = "auto",
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# Different from SAC:
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# projection: BaseProjectionLayer = BaseProjectionLayer(),
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projection=None,
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_init_setup_model: bool = True,
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):
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super().__init__(
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policy=policy,
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env=env,
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learning_rate=learning_rate,
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buffer_size=buffer_size,
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learning_starts=learning_starts,
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batch_size=batch_size,
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tau=tau,
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gamma=gamma,
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train_freq=train_freq,
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gradient_steps=gradient_steps,
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action_noise=action_noise,
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replay_buffer_class=replay_buffer_class,
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replay_buffer_kwargs=replay_buffer_kwargs,
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policy_kwargs=policy_kwargs,
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tensorboard_log=tensorboard_log,
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verbose=verbose,
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device=device,
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support_multi_env=False,
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monitor_wrapper=True,
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seed=seed,
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use_sde=use_sde,
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sde_sample_freq=sde_sample_freq,
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use_sde_at_warmup=use_sde_at_warmup,
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sde_support=True,
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supported_action_spaces=(gym.spaces.Box),
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)
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self.target_entropy = target_entropy
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self.log_ent_coef = None # type: Optional[th.Tensor]
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# Entropy coefficient / Entropy temperature
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# Inverse of the reward scale
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self.ent_coef = ent_coef
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self.target_update_interval = target_update_interval
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self.ent_coef_optimizer = None
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# Different from SAC:
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# self.projection = projection
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self._global_steps = 0
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self.n_steps = buffer_size
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self.gae_lambda = False
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self.action_coef = action_coef
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if projection != None:
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print('[!] An projection was supplied! Will be ignored!')
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if _init_setup_model:
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self._setup_model()
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def _setup_model(self) -> None:
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super()._setup_model()
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self._create_aliases()
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# Target entropy is used when learning the entropy coefficient
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if self.target_entropy == "auto":
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# automatically set target entropy if needed
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self.target_entropy = - \
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np.prod(self.env.action_space.shape).astype(np.float32)
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else:
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# Force conversion
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# this will also throw an error for unexpected string
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self.target_entropy = float(self.target_entropy)
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# The entropy coefficient or entropy can be learned automatically
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# see Automating Entropy Adjustment for Maximum Entropy RL section
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# of https://arxiv.org/abs/1812.05905
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if isinstance(self.ent_coef, str) and self.ent_coef.startswith("auto"):
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# Default initial value of ent_coef when learned
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init_value = 1.0
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if "_" in self.ent_coef:
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init_value = float(self.ent_coef.split("_")[1])
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assert init_value > 0.0, "The initial value of ent_coef must be greater than 0"
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# Note: we optimize the log of the entropy coeff which is slightly different from the paper
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# as discussed in https://github.com/rail-berkeley/softlearning/issues/37
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self.log_ent_coef = th.log(
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th.ones(1, device=self.device) * init_value).requires_grad_(True)
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self.ent_coef_optimizer = th.optim.Adam(
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[self.log_ent_coef], lr=self.lr_schedule(1))
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else:
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# Force conversion to float
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# this will throw an error if a malformed string (different from 'auto')
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# is passed
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self.ent_coef_tensor = th.tensor(
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float(self.ent_coef)).to(self.device)
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def _create_aliases(self) -> None:
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self.actor = self.policy.actor
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self.critic = self.policy.critic
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self.critic_target = self.policy.critic_target
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def train(self, gradient_steps: int, batch_size: int = 64) -> None:
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# Switch to train mode (this affects batch norm / dropout)
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self.policy.set_training_mode(True)
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# Update optimizers learning rate
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optimizers = [self.actor.optimizer, self.critic.optimizer]
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if self.ent_coef_optimizer is not None:
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optimizers += [self.ent_coef_optimizer]
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# Update learning rate according to lr schedule
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self._update_learning_rate(optimizers)
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ent_coef_losses, ent_coefs = [], []
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actor_losses, critic_losses = [], []
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action_losses = []
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for gradient_step in range(gradient_steps):
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# Sample replay buffer
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replay_data = self.replay_buffer.sample(
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batch_size, env=self._vec_normalize_env)
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# This is new compared to SAC.
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# Calculating the TR-Projections we need to know the step number
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self._global_steps += 1
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# We need to sample because `log_std` may have changed between two gradient steps
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if self.use_sde:
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self.actor.reset_noise()
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#################
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# Orig Code:
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# Action by the current actor for the sampled state
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# actions_pi, log_prob = self.actor.action_log_prob(
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# replay_data.observations)
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# log_prob = log_prob.reshape(-1, 1)
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act = self.actor
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features = act.extract_features(replay_data.observations)
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latent_pi = act.latent_pi(features)
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mean_actions = act.mu_net(latent_pi)
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chol = act.chol_net(latent_pi)
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# Original Implementation to cap the standard deviation
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chol = th.clamp(chol, LOG_STD_MIN, LOG_STD_MAX)
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act.chol = chol
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act_dist = self.actor.action_dist
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# internal A
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if self.use_sde:
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actions_pi = act_dist.actions_from_params(
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mean_actions, chol, latent_sde=latent_pi) # latent_pi = latent_sde
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else:
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actions_pi = act_dist.actions_from_params(
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mean_actions, chol)
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p_dist = act_dist.distribution
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# q_dist = new_dist_like(
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# p_dist, replay_data.means, replay_data.stds)
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#proj_p = self.projection(p_dist, q_dist, self._global_steps)
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proj_p = p_dist
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log_prob = proj_p.log_prob(actions_pi)
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log_prob = log_prob.reshape(-1, 1)
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####################
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ent_coef_loss = None
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if self.ent_coef_optimizer is not None:
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# Important: detach the variable from the graph
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# so we don't change it with other losses
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# see https://github.com/rail-berkeley/softlearning/issues/60
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ent_coef = th.exp(self.log_ent_coef.detach())
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ent_coef_loss = - \
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(self.log_ent_coef * (log_prob +
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self.target_entropy).detach()).mean()
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ent_coef_losses.append(ent_coef_loss.item())
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else:
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ent_coef = self.ent_coef_tensor
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ent_coefs.append(ent_coef.item())
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# Optimize entropy coefficient, also called
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# entropy temperature or alpha in the paper
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if ent_coef_loss is not None:
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self.ent_coef_optimizer.zero_grad()
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ent_coef_loss.backward()
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self.ent_coef_optimizer.step()
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with th.no_grad():
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# Select action according to policy
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next_actions, next_log_prob = self.actor.action_log_prob(
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replay_data.next_observations)
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# Compute the next Q values: min over all critics targets
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next_q_values = th.cat(self.critic_target(
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replay_data.next_observations, next_actions), dim=1)
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next_q_values, _ = th.min(next_q_values, dim=1, keepdim=True)
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# add entropy term
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next_q_values = next_q_values - \
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ent_coef * next_log_prob.reshape(-1, 1)
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# td error + entropy term
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target_q_values = replay_data.rewards + \
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(1 - replay_data.dones) * self.gamma * next_q_values
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# Get current Q-values estimates for each critic network
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# using action from the replay buffer
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current_q_values = self.critic(
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replay_data.observations, replay_data.actions)
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projection_loss = th.zeros(1)
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# 'Principle of least action'
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action_loss = th.mean(th.square(actions_pi))
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action_losses.append(action_loss.item())
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# Compute critic loss
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critic_loss_raw = 0.5 * sum(F.mse_loss(current_q, target_q_values)
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for current_q in current_q_values)
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critic_loss = critic_loss_raw + projection_loss
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critic_losses.append(critic_loss.item())
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# Optimize the critic
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self.critic.optimizer.zero_grad()
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critic_loss.backward()
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self.critic.optimizer.step()
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# Compute actor loss
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# Alternative: actor_loss = th.mean(log_prob - qf1_pi)
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# Mean over all critic networks
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q_values_pi = th.cat(self.critic(
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replay_data.observations, actions_pi), dim=1)
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min_qf_pi, _ = th.min(q_values_pi, dim=1, keepdim=True)
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actor_loss = (ent_coef * log_prob - min_qf_pi +
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self.action_coef * action_loss).mean()
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actor_losses.append(actor_loss.item())
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# Optimize the actor
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self.actor.optimizer.zero_grad()
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actor_loss.backward()
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self.actor.optimizer.step()
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# Update target networks
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if gradient_step % self.target_update_interval == 0:
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polyak_update(self.critic.parameters(),
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self.critic_target.parameters(), self.tau)
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self._n_updates += gradient_steps
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self.logger.record("train/n_updates",
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self._n_updates, exclude="tensorboard")
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self.logger.record("train/action_loss", np.mean(action_losses))
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self.logger.record("train/ent_coef", np.mean(ent_coefs))
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self.logger.record("train/ssf", self.sde_sample_freq)
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self.logger.record("train/actor_loss", np.mean(actor_losses))
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self.logger.record("train/critic_loss", np.mean(critic_losses))
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if len(ent_coef_losses) > 0:
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self.logger.record("train/ent_coef_loss", np.mean(ent_coef_losses))
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pol = self.policy.actor
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if hasattr(pol, "log_std"):
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self.logger.record(
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"train/std", th.exp(pol.log_std).mean().item())
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elif hasattr(pol, "chol"):
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chol = pol.chol
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if len(chol.shape) == 1:
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self.logger.record(
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"train/std", th.mean(chol).mean().item())
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elif len(chol.shape) == 2:
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self.logger.record(
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"train/std", th.mean(th.sqrt(th.diagonal(chol.T @ chol, dim1=-2, dim2=-1))).mean().item())
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else:
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self.logger.record(
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"train/std", th.mean(th.sqrt(th.diagonal(chol.mT @ chol, dim1=-2, dim2=-1))).mean().item())
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def learn(
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self,
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total_timesteps: int,
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callback: MaybeCallback = None,
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log_interval: int = 4,
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eval_env: Optional[GymEnv] = None,
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eval_freq: int = -1,
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n_eval_episodes: int = 5,
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tb_log_name: str = "SAC",
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eval_log_path: Optional[str] = None,
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reset_num_timesteps: bool = True,
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) -> OffPolicyAlgorithm:
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return super().learn(
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total_timesteps=total_timesteps,
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callback=callback,
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log_interval=log_interval,
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eval_env=eval_env,
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eval_freq=eval_freq,
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n_eval_episodes=n_eval_episodes,
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tb_log_name=tb_log_name,
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eval_log_path=eval_log_path,
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reset_num_timesteps=reset_num_timesteps,
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)
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def _excluded_save_params(self) -> List[str]:
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return super()._excluded_save_params() + ["actor", "critic", "critic_target"]
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def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
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state_dicts = ["policy", "actor.optimizer", "critic.optimizer"]
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if self.ent_coef_optimizer is not None:
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saved_pytorch_variables = ["log_ent_coef"]
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state_dicts.append("ent_coef_optimizer")
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else:
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saved_pytorch_variables = ["ent_coef_tensor"]
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return state_dicts, saved_pytorch_variables
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