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import logging
from time import perf_counter
from typing import List, Optional, TypeVar
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.tensorboard.writer import SummaryWriter
from rl_algo_impls.shared.algorithm import Algorithm
from rl_algo_impls.shared.callbacks import Callback
from rl_algo_impls.shared.gae import compute_advantages
from rl_algo_impls.shared.policy.actor_critic import ActorCritic
from rl_algo_impls.shared.schedule import schedule, update_learning_rate
from rl_algo_impls.shared.stats import log_scalars
from rl_algo_impls.wrappers.vectorable_wrapper import (
VecEnv,
single_action_space,
single_observation_space,
)
A2CSelf = TypeVar("A2CSelf", bound="A2C")
class A2C(Algorithm):
def __init__(
self,
policy: ActorCritic,
env: VecEnv,
device: torch.device,
tb_writer: SummaryWriter,
learning_rate: float = 7e-4,
learning_rate_decay: str = "none",
n_steps: int = 5,
gamma: float = 0.99,
gae_lambda: float = 1.0,
ent_coef: float = 0.0,
ent_coef_decay: str = "none",
vf_coef: float = 0.5,
max_grad_norm: float = 0.5,
rms_prop_eps: float = 1e-5,
use_rms_prop: bool = True,
sde_sample_freq: int = -1,
normalize_advantage: bool = False,
) -> None:
super().__init__(policy, env, device, tb_writer)
self.policy = policy
self.lr_schedule = schedule(learning_rate_decay, learning_rate)
if use_rms_prop:
self.optimizer = torch.optim.RMSprop(
policy.parameters(), lr=learning_rate, eps=rms_prop_eps
)
else:
self.optimizer = torch.optim.Adam(policy.parameters(), lr=learning_rate)
self.n_steps = n_steps
self.gamma = gamma
self.gae_lambda = gae_lambda
self.vf_coef = vf_coef
self.ent_coef_schedule = schedule(ent_coef_decay, ent_coef)
self.max_grad_norm = max_grad_norm
self.sde_sample_freq = sde_sample_freq
self.normalize_advantage = normalize_advantage
def learn(
self: A2CSelf,
train_timesteps: int,
callbacks: Optional[List[Callback]] = None,
total_timesteps: Optional[int] = None,
start_timesteps: int = 0,
) -> A2CSelf:
if total_timesteps is None:
total_timesteps = train_timesteps
assert start_timesteps + train_timesteps <= total_timesteps
epoch_dim = (self.n_steps, self.env.num_envs)
step_dim = (self.env.num_envs,)
obs_space = single_observation_space(self.env)
act_space = single_action_space(self.env)
obs = np.zeros(epoch_dim + obs_space.shape, dtype=obs_space.dtype)
actions = np.zeros(epoch_dim + act_space.shape, dtype=act_space.dtype)
rewards = np.zeros(epoch_dim, dtype=np.float32)
episode_starts = np.zeros(epoch_dim, dtype=np.bool8)
values = np.zeros(epoch_dim, dtype=np.float32)
logprobs = np.zeros(epoch_dim, dtype=np.float32)
next_obs = self.env.reset()
next_episode_starts = np.full(step_dim, True, dtype=np.bool8)
timesteps_elapsed = start_timesteps
while timesteps_elapsed < start_timesteps + train_timesteps:
start_time = perf_counter()
progress = timesteps_elapsed / total_timesteps
ent_coef = self.ent_coef_schedule(progress)
learning_rate = self.lr_schedule(progress)
update_learning_rate(self.optimizer, learning_rate)
log_scalars(
self.tb_writer,
"charts",
{
"ent_coef": ent_coef,
"learning_rate": learning_rate,
},
timesteps_elapsed,
)
self.policy.eval()
self.policy.reset_noise()
for s in range(self.n_steps):
timesteps_elapsed += self.env.num_envs
if self.sde_sample_freq > 0 and s > 0 and s % self.sde_sample_freq == 0:
self.policy.reset_noise()
obs[s] = next_obs
episode_starts[s] = next_episode_starts
actions[s], values[s], logprobs[s], clamped_action = self.policy.step(
next_obs
)
next_obs, rewards[s], next_episode_starts, _ = self.env.step(
clamped_action
)
advantages = compute_advantages(
rewards,
values,
episode_starts,
next_episode_starts,
next_obs,
self.policy,
self.gamma,
self.gae_lambda,
)
returns = advantages + values
b_obs = torch.tensor(obs.reshape((-1,) + obs_space.shape)).to(self.device)
b_actions = torch.tensor(actions.reshape((-1,) + act_space.shape)).to(
self.device
)
b_advantages = torch.tensor(advantages.reshape(-1)).to(self.device)
b_returns = torch.tensor(returns.reshape(-1)).to(self.device)
if self.normalize_advantage:
b_advantages = (b_advantages - b_advantages.mean()) / (
b_advantages.std() + 1e-8
)
self.policy.train()
logp_a, entropy, v = self.policy(b_obs, b_actions)
pi_loss = -(b_advantages * logp_a).mean()
value_loss = F.mse_loss(b_returns, v)
entropy_loss = -entropy.mean()
loss = pi_loss + self.vf_coef * value_loss + ent_coef * entropy_loss
self.optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
self.optimizer.step()
y_pred = values.reshape(-1)
y_true = returns.reshape(-1)
var_y = np.var(y_true).item()
explained_var = (
np.nan if var_y == 0 else 1 - np.var(y_true - y_pred).item() / var_y
)
end_time = perf_counter()
rollout_steps = self.n_steps * self.env.num_envs
self.tb_writer.add_scalar(
"train/steps_per_second",
(rollout_steps) / (end_time - start_time),
timesteps_elapsed,
)
log_scalars(
self.tb_writer,
"losses",
{
"loss": loss.item(),
"pi_loss": pi_loss.item(),
"v_loss": value_loss.item(),
"entropy_loss": entropy_loss.item(),
"explained_var": explained_var,
},
timesteps_elapsed,
)
if callbacks:
if not all(
c.on_step(timesteps_elapsed=rollout_steps) for c in callbacks
):
logging.info(
f"Callback terminated training at {timesteps_elapsed} timesteps"
)
break
return self
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