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import numpy as np
import torch
import torch.nn as nn
from collections import defaultdict
from dataclasses import dataclass, asdict
from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs
from torch.optim import Adam
from torch.utils.tensorboard.writer import SummaryWriter
from typing import Optional, Sequence, TypeVar
from shared.algorithm import Algorithm
from shared.callbacks.callback import Callback
from shared.gae import compute_rtg_and_advantage, compute_advantage
from shared.trajectory import Trajectory, TrajectoryAccumulator
from vpg.policy import VPGActorCritic
@dataclass
class TrainEpochStats:
pi_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:
tb_writer.add_scalars("losses", asdict(self), 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,
update_rtg_between_v_iters: bool = False,
) -> 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.update_rtg_between_v_iters = update_rtg_between_v_iters
def learn(
self: VanillaPolicyGradientSelf,
total_timesteps: int,
callback: Optional[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(
f"Epoch: {epoch_cnt} | "
f"Pi Loss: {round(epoch_stats.pi_loss, 2)} | "
f"V Loss: {round(epoch_stats.v_loss, 2)} | "
f"Total Steps: {timesteps_elapsed}"
)
if callback:
callback.on_step(timesteps_elapsed=epoch_steps)
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(
trajectories, self.policy, self.gamma, self.gae_lambda, self.device
)
pi_loss = self._update_pi(obs, act, adv)
v_loss = 0
for _ in range(self.train_v_iters):
if self.update_rtg_between_v_iters:
rtg = compute_advantage(
trajectories, self.policy, self.gamma, self.gae_lambda, self.device
)
v_loss = self._update_v(obs, rtg)
return TrainEpochStats(pi_loss, v_loss)
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
def _update_pi(
self, obs: torch.Tensor, act: torch.Tensor, adv: torch.Tensor
) -> float:
self.pi_optim.zero_grad()
_, logp, _ = self.policy.pi(obs, act)
pi_loss = -(logp * adv).mean()
pi_loss.backward()
nn.utils.clip_grad_norm_(self.policy.pi.parameters(), self.max_grad_norm)
self.pi_optim.step()
return pi_loss.item()
def _update_v(self, obs: torch.Tensor, rtg: torch.Tensor) -> float:
self.val_optim.zero_grad()
v = self.policy.v(obs)
v_loss = ((v - rtg) ** 2).mean()
v_loss.backward()
nn.utils.clip_grad_norm_(self.policy.v.parameters(), self.max_grad_norm)
self.val_optim.step()
return v_loss.item()
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