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import logging
from collections import defaultdict
from dataclasses import asdict, dataclass
from typing import List, Optional, Sequence, TypeVar
import numpy as np
import torch
import torch.nn as nn
from torch.optim import Adam
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_rtg_and_advantage_from_trajectories
from rl_algo_impls.shared.trajectory import Trajectory, TrajectoryAccumulator
from rl_algo_impls.vpg.policy import VPGActorCritic
from rl_algo_impls.wrappers.vectorable_wrapper import VecEnv
@dataclass
class TrainEpochStats:
pi_loss: float
entropy_loss: float
v_loss: float
envs_with_done: int = 0
episodes_done: int = 0
def write_to_tensorboard(self, tb_writer: SummaryWriter, global_step: int) -> None:
for name, value in asdict(self).items():
tb_writer.add_scalar(f"losses/{name}", value, global_step=global_step)
class VPGTrajectoryAccumulator(TrajectoryAccumulator):
def __init__(self, num_envs: int) -> None:
super().__init__(num_envs, trajectory_class=Trajectory)
self.completed_per_env: defaultdict[int, int] = defaultdict(int)
def on_done(self, env_idx: int, trajectory: Trajectory) -> None:
self.completed_per_env[env_idx] += 1
VanillaPolicyGradientSelf = TypeVar(
"VanillaPolicyGradientSelf", bound="VanillaPolicyGradient"
)
class VanillaPolicyGradient(Algorithm):
def __init__(
self,
policy: VPGActorCritic,
env: VecEnv,
device: torch.device,
tb_writer: SummaryWriter,
gamma: float = 0.99,
pi_lr: float = 3e-4,
val_lr: float = 1e-3,
train_v_iters: int = 80,
gae_lambda: float = 0.97,
max_grad_norm: float = 10.0,
n_steps: int = 4_000,
sde_sample_freq: int = -1,
ent_coef: float = 0.0,
) -> None:
super().__init__(policy, env, device, tb_writer)
self.policy = policy
self.gamma = gamma
self.gae_lambda = gae_lambda
self.pi_optim = Adam(self.policy.pi.parameters(), lr=pi_lr)
self.val_optim = Adam(self.policy.v.parameters(), lr=val_lr)
self.max_grad_norm = max_grad_norm
self.n_steps = n_steps
self.train_v_iters = train_v_iters
self.sde_sample_freq = sde_sample_freq
self.ent_coef = ent_coef
def learn(
self: VanillaPolicyGradientSelf,
total_timesteps: int,
callbacks: Optional[List[Callback]] = None,
) -> VanillaPolicyGradientSelf:
timesteps_elapsed = 0
epoch_cnt = 0
while timesteps_elapsed < total_timesteps:
epoch_cnt += 1
accumulator = self._collect_trajectories()
epoch_stats = self.train(accumulator.all_trajectories)
epoch_stats.envs_with_done = len(accumulator.completed_per_env)
epoch_stats.episodes_done = sum(accumulator.completed_per_env.values())
epoch_steps = accumulator.n_timesteps()
timesteps_elapsed += epoch_steps
epoch_stats.write_to_tensorboard(
self.tb_writer, global_step=timesteps_elapsed
)
print(
" | ".join(
[
f"Epoch: {epoch_cnt}",
f"Pi Loss: {round(epoch_stats.pi_loss, 2)}",
f"Epoch Loss: {round(epoch_stats.entropy_loss, 2)}",
f"V Loss: {round(epoch_stats.v_loss, 2)}",
f"Total Steps: {timesteps_elapsed}",
]
)
)
if callbacks:
if not all(c.on_step(timesteps_elapsed=epoch_steps) for c in callbacks):
logging.info(
f"Callback terminated training at {timesteps_elapsed} timesteps"
)
break
return self
def train(self, trajectories: Sequence[Trajectory]) -> TrainEpochStats:
self.policy.train()
obs = torch.as_tensor(
np.concatenate([np.array(t.obs) for t in trajectories]), device=self.device
)
act = torch.as_tensor(
np.concatenate([np.array(t.act) for t in trajectories]), device=self.device
)
rtg, adv = compute_rtg_and_advantage_from_trajectories(
trajectories, self.policy, self.gamma, self.gae_lambda, self.device
)
_, logp, entropy = self.policy.pi(obs, act)
pi_loss = -(logp * adv).mean()
entropy_loss = entropy.mean()
actor_loss = pi_loss - self.ent_coef * entropy_loss
self.pi_optim.zero_grad()
actor_loss.backward()
nn.utils.clip_grad_norm_(self.policy.pi.parameters(), self.max_grad_norm)
self.pi_optim.step()
v_loss = 0
for _ in range(self.train_v_iters):
v = self.policy.v(obs)
v_loss = ((v - rtg) ** 2).mean()
self.val_optim.zero_grad()
v_loss.backward()
nn.utils.clip_grad_norm_(self.policy.v.parameters(), self.max_grad_norm)
self.val_optim.step()
return TrainEpochStats(
pi_loss.item(),
entropy_loss.item(),
v_loss.item(), # type: ignore
)
def _collect_trajectories(self) -> VPGTrajectoryAccumulator:
self.policy.eval()
obs = self.env.reset()
accumulator = VPGTrajectoryAccumulator(self.env.num_envs)
self.policy.reset_noise()
for i in range(self.n_steps):
if self.sde_sample_freq > 0 and i > 0 and i % self.sde_sample_freq == 0:
self.policy.reset_noise()
action, value, _, clamped_action = self.policy.step(obs)
next_obs, reward, done, _ = self.env.step(clamped_action)
accumulator.step(obs, action, next_obs, reward, done, value)
obs = next_obs
return accumulator
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