genrl/agents/deep/a2c/a2c.py
from typing import Any, Dict
import gym
import torch
import torch.optim as opt
from torch.nn import functional as F
from genrl.agents.deep.base import OnPolicyAgent
from genrl.utils import (
compute_returns_and_advantage,
get_env_properties,
get_model,
safe_mean,
)
class A2C(OnPolicyAgent):
"""Advantage Actor Critic algorithm (A2C)
The synchronous version of A3C
Paper: https://arxiv.org/abs/1602.01783
Attributes:
network (str): The network type of the Q-value function.
Supported types: ["cnn", "mlp"]
env (Environment): The environment that the agent is supposed to act on
create_model (bool): Whether the model of the algo should be created when initialised
batch_size (int): Mini batch size for loading experiences
gamma (float): The discount factor for rewards
layers (:obj:`tuple` of :obj:`int`): Layers in the Neural Network
of the Q-value function
shared_layers(:obj:`tuple` of :obj:`int`): Sizes of shared layers in Actor Critic if using
lr_policy (float): Learning rate for the policy/actor
lr_value (float): Learning rate for the critic
rollout_size (int): Capacity of the Replay Buffer
buffer_type (str): Choose the type of Buffer: ["rollout"]
noise (:obj:`ActionNoise`): Action Noise function added to aid in exploration
noise_std (float): Standard deviation of the action noise distribution
value_coeff (float): Ratio of magnitude of value updates to policy updates
entropy_coeff (float): Ratio of magnitude of entropy updates to policy updates
seed (int): Seed for randomness
render (bool): Should the env be rendered during training?
device (str): Hardware being used for training. Options:
["cuda" -> GPU, "cpu" -> CPU]
"""
def __init__(
self,
*args,
noise: Any = None,
noise_std: float = 0.1,
value_coeff: float = 0.5,
entropy_coeff: float = 0.01,
**kwargs
):
super(A2C, self).__init__(*args, **kwargs)
self.noise = noise
self.noise_std = noise_std
self.value_coeff = value_coeff
self.entropy_coeff = entropy_coeff
self.empty_logs()
if self.create_model:
self._create_model()
def _create_model(self) -> None:
"""Function to initialize Actor-Critic architecture
This will create the Actor-Critic net for the agent and initialise the action noise
"""
state_dim, action_dim, discrete, action_lim = get_env_properties(
self.env, self.network
)
if isinstance(self.network, str):
arch_type = self.network
if self.shared_layers is not None:
arch_type += "s"
self.ac = get_model("ac", arch_type)(
state_dim,
action_dim,
shared_layers=self.shared_layers,
policy_layers=self.policy_layers,
value_layers=self.value_layers,
val_type="V",
discrete=discrete,
action_lim=action_lim,
).to(self.device)
else:
self.ac = self.network.to(self.device)
if self.noise is not None:
self.noise = self.noise(
torch.zeros(action_dim), self.noise_std * torch.ones(action_dim)
)
actor_params, critic_params = self.ac.get_params()
self.optimizer_policy = opt.Adam(critic_params, lr=self.lr_policy)
self.optimizer_value = opt.Adam(actor_params, lr=self.lr_value)
def select_action(
self, state: torch.Tensor, deterministic: bool = False
) -> torch.Tensor:
"""Select action given state
Action Selection for On Policy Agents with Actor Critic
Args:
state (:obj:`torch.Tensor`): Current state of the environment
deterministic (bool): Should the policy be deterministic or stochastic
Returns:
action (:obj:`torch.Tensor`): Action taken by the agent
value (:obj:`torch.Tensor`): Value of given state
log_prob (:obj:`torch.Tensor`): Log probability of selected action
"""
# create distribution based on actor output
action, dist = self.ac.get_action(state, deterministic=deterministic)
value = self.ac.get_value(state)
return action.detach(), value, dist.log_prob(action).cpu()
def get_traj_loss(self, values: torch.Tensor, dones: torch.Tensor) -> None:
"""Get loss from trajectory traversed by agent during rollouts
Computes the returns and advantages needed for calculating loss
Args:
values (:obj:`torch.Tensor`): Values of states encountered during the rollout
dones (:obj:`list` of bool): Game over statuses of each environment
"""
compute_returns_and_advantage(
self.rollout, values.detach().cpu().numpy(), dones.cpu().numpy()
)
def evaluate_actions(self, states: torch.Tensor, actions: torch.Tensor):
"""Evaluates actions taken by actor
Actions taken by actor and their respective states are analysed to get
log probabilities and values from critics
Args:
states (:obj:`torch.Tensor`): States encountered in rollout
actions (:obj:`torch.Tensor`): Actions taken in response to respective states
Returns:
values (:obj:`torch.Tensor`): Values of states encountered during the rollout
log_probs (:obj:`torch.Tensor`): Log of action probabilities given a state
"""
states, actions = states.to(self.device), actions.to(self.device)
_, dist = self.ac.get_action(states, deterministic=False)
values = self.ac.get_value(states)
return values, dist.log_prob(actions).cpu(), dist.entropy().cpu()
def update_params(self) -> None:
"""Updates the the A2C network
Function to update the A2C actor-critic architecture
"""
for rollout in self.rollout.get(self.batch_size):
actions = rollout.actions
if isinstance(self.env.action_space, gym.spaces.Discrete):
actions = actions.long().flatten()
values, log_prob, entropy = self.evaluate_actions(
rollout.observations, actions
)
policy_loss = rollout.advantages * log_prob
policy_loss = -torch.mean(policy_loss)
self.logs["policy_loss"].append(policy_loss.item())
value_loss = self.value_coeff * F.mse_loss(rollout.returns, values.cpu())
self.logs["value_loss"].append(torch.mean(value_loss).item())
entropy_loss = -torch.mean(entropy) # Change this to entropy
self.logs["policy_entropy"].append(entropy_loss.item())
actor_loss = policy_loss + self.entropy_coeff * entropy_loss
self.optimizer_policy.zero_grad()
actor_loss.backward()
torch.nn.utils.clip_grad_norm_(self.ac.actor.parameters(), 0.5)
self.optimizer_policy.step()
self.optimizer_value.zero_grad()
value_loss.backward()
torch.nn.utils.clip_grad_norm_(self.ac.critic.parameters(), 0.5)
self.optimizer_value.step()
def get_hyperparams(self) -> Dict[str, Any]:
"""Get relevant hyperparameters to save
Returns:
hyperparams (:obj:`dict`): Hyperparameters to be saved
weights (:obj:`torch.Tensor`): Neural network weights
"""
hyperparams = {
"network": self.network,
"batch_size": self.batch_size,
"gamma": self.gamma,
"lr_policy": self.lr_policy,
"lr_value": self.lr_value,
"rollout_size": self.rollout_size,
}
return hyperparams, self.ac.state_dict()
def _load_weights(self, weights) -> None:
"""Load weights for the agent from pretrained model
Args:
weights (:obj:`torch.Tensor`): neural net weights
"""
self.ac.load_state_dict(weights)
def get_logging_params(self) -> Dict[str, Any]:
"""Gets relevant parameters for logging
Returns:
logs (:obj:`dict`): Logging parameters for monitoring training
"""
logs = {
"policy_loss": safe_mean(self.logs["policy_loss"]),
"value_loss": safe_mean(self.logs["value_loss"]),
"policy_entropy": safe_mean(self.logs["policy_entropy"]),
"mean_reward": safe_mean(self.rewards),
}
self.empty_logs()
return logs
def empty_logs(self):
"""Empties logs"""
self.logs = {}
self.logs["policy_loss"] = []
self.logs["value_loss"] = []
self.logs["policy_entropy"] = []
self.rewards = []