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import numpy as np
import torch
import torch.nn as nn
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, NamedTuple, TypeVar
from shared.algorithm import Algorithm
from shared.callbacks.callback import Callback
from shared.trajectory import Trajectory
from shared.utils import discounted_cumsum
from vpg.policy import VPGActorCritic
class TrajectoryAccumulator:
def __init__(self, num_envs: int, goal_steps: int):
self.num_envs = num_envs
self.trajectories = []
self.current_trajectories = [Trajectory() for _ in range(num_envs)]
self.steps_per_env = int(np.ceil(goal_steps / num_envs))
self.step_idx = 0
self.envs_done: set[int] = set()
def step(
self,
obs: VecEnvObs,
action: np.ndarray,
reward: np.ndarray,
done: np.ndarray,
val: np.ndarray,
) -> None:
assert isinstance(obs, np.ndarray)
self.step_idx += 1
for i, trajectory in enumerate(self.current_trajectories):
trajectory.add(obs[i], action[i], reward[i], val[i])
if done[i]:
# TODO: Eventually take advantage of terminated/truncated
# differentiation in later versions of gym.
trajectory.terminated = True
self.trajectories.append(trajectory)
self.current_trajectories[i] = Trajectory()
if self.step_idx >= self.steps_per_env:
self.envs_done.add(i)
def is_done(self) -> bool:
return len(self.envs_done) == self.num_envs
def n_timesteps(self) -> int:
return np.sum([len(t) for t in self.trajectories]).item()
class RtgAdvantage(NamedTuple):
rewards_to_go: torch.Tensor
advantage: torch.Tensor
class TrainEpochStats(NamedTuple):
pi_loss: float
v_loss: float
def write_to_tensorboard(self, tb_writer: SummaryWriter, global_step: int) -> None:
tb_writer.add_scalars("losses", self._asdict(), global_step=global_step)
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,
lam: float = 0.97,
max_grad_norm: float = 10.0,
steps_per_epoch: int = 4_000,
) -> None:
super().__init__(policy, env, device, tb_writer)
self.policy = policy
self.gamma = gamma
self.lam = lam
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.steps_per_epoch = steps_per_epoch
self.train_v_iters = train_v_iters
def learn(
self: VanillaPolicyGradientSelf,
total_timesteps: int,
callback: Optional[Callback] = None,
) -> VanillaPolicyGradientSelf:
self.policy.train(True)
obs = self.env.reset()
timesteps_elapsed = 0
epoch_cnt = 0
while timesteps_elapsed < total_timesteps:
epoch_cnt += 1
accumulator = self._collect_trajectories(obs)
epoch_stats = self.train(accumulator.trajectories)
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:
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 = self._compute_rtg_and_advantage(trajectories)
pi_loss = self._update_pi(obs, act, adv)
v_loss = 0
for _ in range(self.train_v_iters):
v_loss = self._update_v(obs, rtg)
return TrainEpochStats(pi_loss, v_loss)
def _collect_trajectories(self, obs: VecEnvObs) -> TrajectoryAccumulator:
accumulator = TrajectoryAccumulator(self.env.num_envs, self.steps_per_epoch)
while not accumulator.is_done():
action, value, _, clamped_action = self.policy.step(obs)
next_obs, reward, done, _ = self.env.step(clamped_action)
accumulator.step(obs, action, reward, done, value)
obs = next_obs
return accumulator
def _compute_rtg_and_advantage(
self, trajectories: Sequence[Trajectory]
) -> RtgAdvantage:
rewards_to_go = []
advantage = []
for traj in trajectories:
last_val = 0 if traj.terminated else self.policy.step(traj.obs[-1]).v
rew = np.append(np.array(traj.rew), last_val)
v = np.append(np.array(traj.v), last_val)
rewards_to_go.append(discounted_cumsum(rew, self.gamma)[:-1])
deltas = rew[:-1] + self.gamma * v[1:] - v[:-1]
advantage.append(discounted_cumsum(deltas, self.gamma * self.lam))
return RtgAdvantage(
torch.as_tensor(
np.concatenate(rewards_to_go), dtype=torch.float32, device=self.device
),
torch.as_tensor(
np.concatenate(advantage), dtype=torch.float32, device=self.device
),
)
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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