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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from dataclasses import asdict, dataclass, field
from time import perf_counter
from torch.optim import Adam
from torch.utils.tensorboard.writer import SummaryWriter
from typing import List, Optional, NamedTuple, TypeVar
from shared.algorithm import Algorithm
from shared.callbacks.callback import Callback
from shared.gae import compute_advantage, compute_rtg_and_advantage
from shared.policy.on_policy import ActorCritic
from shared.schedule import constant_schedule, linear_schedule, update_learning_rate
from shared.trajectory import Trajectory, TrajectoryAccumulator
from wrappers.vectorable_wrapper import VecEnv, VecEnvObs
@dataclass
class PPOTrajectory(Trajectory):
logp_a: List[float] = field(default_factory=list)
def add(
self,
obs: np.ndarray,
act: np.ndarray,
next_obs: np.ndarray,
rew: float,
terminated: bool,
v: float,
logp_a: float,
):
super().add(obs, act, next_obs, rew, terminated, v)
self.logp_a.append(logp_a)
class PPOTrajectoryAccumulator(TrajectoryAccumulator):
def __init__(self, num_envs: int) -> None:
super().__init__(num_envs, PPOTrajectory)
def step(
self,
obs: VecEnvObs,
action: np.ndarray,
next_obs: VecEnvObs,
reward: np.ndarray,
done: np.ndarray,
val: np.ndarray,
logp_a: np.ndarray,
) -> None:
super().step(obs, action, next_obs, reward, done, val, logp_a)
class TrainStepStats(NamedTuple):
loss: float
pi_loss: float
v_loss: float
entropy_loss: float
approx_kl: float
clipped_frac: float
val_clipped_frac: float
@dataclass
class TrainStats:
loss: float
pi_loss: float
v_loss: float
entropy_loss: float
approx_kl: float
clipped_frac: float
val_clipped_frac: float
explained_var: float
def __init__(self, step_stats: List[TrainStepStats], explained_var: float) -> None:
self.loss = np.mean([s.loss for s in step_stats]).item()
self.pi_loss = np.mean([s.pi_loss for s in step_stats]).item()
self.v_loss = np.mean([s.v_loss for s in step_stats]).item()
self.entropy_loss = np.mean([s.entropy_loss for s in step_stats]).item()
self.approx_kl = np.mean([s.approx_kl for s in step_stats]).item()
self.clipped_frac = np.mean([s.clipped_frac for s in step_stats]).item()
self.val_clipped_frac = np.mean([s.val_clipped_frac for s in step_stats]).item()
self.explained_var = explained_var
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)
def __repr__(self) -> str:
return " | ".join(
[
f"Loss: {round(self.loss, 2)}",
f"Pi L: {round(self.pi_loss, 2)}",
f"V L: {round(self.v_loss, 2)}",
f"E L: {round(self.entropy_loss, 2)}",
f"Apx KL Div: {round(self.approx_kl, 2)}",
f"Clip Frac: {round(self.clipped_frac, 2)}",
f"Val Clip Frac: {round(self.val_clipped_frac, 2)}",
]
)
PPOSelf = TypeVar("PPOSelf", bound="PPO")
class PPO(Algorithm):
def __init__(
self,
policy: ActorCritic,
env: VecEnv,
device: torch.device,
tb_writer: SummaryWriter,
learning_rate: float = 3e-4,
learning_rate_decay: str = "none",
n_steps: int = 2048,
batch_size: int = 64,
n_epochs: int = 10,
gamma: float = 0.99,
gae_lambda: float = 0.95,
clip_range: float = 0.2,
clip_range_decay: str = "none",
clip_range_vf: Optional[float] = None,
clip_range_vf_decay: str = "none",
normalize_advantage: bool = True,
ent_coef: float = 0.0,
ent_coef_decay: str = "none",
vf_coef: float = 0.5,
ppo2_vf_coef_halving: bool = False,
max_grad_norm: float = 0.5,
update_rtg_between_epochs: bool = False,
sde_sample_freq: int = -1,
) -> None:
super().__init__(policy, env, device, tb_writer)
self.policy = policy
self.gamma = gamma
self.gae_lambda = gae_lambda
self.optimizer = Adam(self.policy.parameters(), lr=learning_rate, eps=1e-7)
self.lr_schedule = (
linear_schedule(learning_rate, 0)
if learning_rate_decay == "linear"
else constant_schedule(learning_rate)
)
self.max_grad_norm = max_grad_norm
self.clip_range_schedule = (
linear_schedule(clip_range, 0)
if clip_range_decay == "linear"
else constant_schedule(clip_range)
)
self.clip_range_vf_schedule = None
if clip_range_vf:
self.clip_range_vf_schedule = (
linear_schedule(clip_range_vf, 0)
if clip_range_vf_decay == "linear"
else constant_schedule(clip_range_vf)
)
self.normalize_advantage = normalize_advantage
self.ent_coef_schedule = (
linear_schedule(ent_coef, 0)
if ent_coef_decay == "linear"
else constant_schedule(ent_coef)
)
self.vf_coef = vf_coef
self.ppo2_vf_coef_halving = ppo2_vf_coef_halving
self.n_steps = n_steps
self.batch_size = batch_size
self.n_epochs = n_epochs
self.sde_sample_freq = sde_sample_freq
self.update_rtg_between_epochs = update_rtg_between_epochs
def learn(
self: PPOSelf,
total_timesteps: int,
callback: Optional[Callback] = None,
) -> PPOSelf:
obs = self.env.reset()
ts_elapsed = 0
while ts_elapsed < total_timesteps:
start_time = perf_counter()
accumulator = self._collect_trajectories(obs)
rollout_steps = self.n_steps * self.env.num_envs
ts_elapsed += rollout_steps
progress = ts_elapsed / total_timesteps
train_stats = self.train(accumulator.all_trajectories, progress, ts_elapsed)
train_stats.write_to_tensorboard(self.tb_writer, ts_elapsed)
end_time = perf_counter()
self.tb_writer.add_scalar(
"train/steps_per_second",
rollout_steps / (end_time - start_time),
ts_elapsed,
)
if callback:
callback.on_step(timesteps_elapsed=rollout_steps)
return self
def _collect_trajectories(self, obs: VecEnvObs) -> PPOTrajectoryAccumulator:
self.policy.eval()
accumulator = PPOTrajectoryAccumulator(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, logp_a, clamped_action = self.policy.step(obs)
next_obs, reward, done, _ = self.env.step(clamped_action)
accumulator.step(obs, action, next_obs, reward, done, value, logp_a)
obs = next_obs
return accumulator
def train(
self, trajectories: List[PPOTrajectory], progress: float, timesteps_elapsed: int
) -> TrainStats:
self.policy.train()
learning_rate = self.lr_schedule(progress)
update_learning_rate(self.optimizer, learning_rate)
self.tb_writer.add_scalar(
"charts/learning_rate",
self.optimizer.param_groups[0]["lr"],
timesteps_elapsed,
)
pi_clip = self.clip_range_schedule(progress)
self.tb_writer.add_scalar("charts/pi_clip", pi_clip, timesteps_elapsed)
if self.clip_range_vf_schedule:
v_clip = self.clip_range_vf_schedule(progress)
self.tb_writer.add_scalar("charts/v_clip", v_clip, timesteps_elapsed)
else:
v_clip = None
ent_coef = self.ent_coef_schedule(progress)
self.tb_writer.add_scalar("charts/ent_coef", ent_coef, timesteps_elapsed)
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
)
orig_v = torch.as_tensor(
np.concatenate([np.array(t.v) for t in trajectories]), device=self.device
)
orig_logp_a = torch.as_tensor(
np.concatenate([np.array(t.logp_a) for t in trajectories]),
device=self.device,
)
step_stats = []
for _ in range(self.n_epochs):
step_stats.clear()
if self.update_rtg_between_epochs:
rtg, adv = compute_rtg_and_advantage(
trajectories, self.policy, self.gamma, self.gae_lambda, self.device
)
else:
adv = compute_advantage(
trajectories, self.policy, self.gamma, self.gae_lambda, self.device
)
idxs = torch.randperm(len(obs))
for i in range(0, len(obs), self.batch_size):
mb_idxs = idxs[i : i + self.batch_size]
mb_adv = adv[mb_idxs]
if self.normalize_advantage:
mb_adv = (mb_adv - mb_adv.mean(-1)) / (mb_adv.std(-1) + 1e-8)
step_stats.append(
self._train_step(
pi_clip,
v_clip,
ent_coef,
obs[mb_idxs],
act[mb_idxs],
rtg[mb_idxs],
mb_adv,
orig_v[mb_idxs],
orig_logp_a[mb_idxs],
)
)
y_pred, y_true = orig_v.cpu().numpy(), rtg.cpu().numpy()
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
)
return TrainStats(step_stats, explained_var)
def _train_step(
self,
pi_clip: float,
v_clip: Optional[float],
ent_coef: float,
obs: torch.Tensor,
act: torch.Tensor,
rtg: torch.Tensor,
adv: torch.Tensor,
orig_v: torch.Tensor,
orig_logp_a: torch.Tensor,
) -> TrainStepStats:
logp_a, entropy, v = self.policy(obs, act)
logratio = logp_a - orig_logp_a
ratio = torch.exp(logratio)
clip_ratio = torch.clamp(ratio, min=1 - pi_clip, max=1 + pi_clip)
pi_loss = torch.maximum(-ratio * adv, -clip_ratio * adv).mean()
v_loss_unclipped = (v - rtg) ** 2
if v_clip:
v_loss_clipped = (
orig_v + torch.clamp(v - orig_v, -v_clip, v_clip) - rtg
) ** 2
v_loss = torch.max(v_loss_unclipped, v_loss_clipped).mean()
else:
v_loss = v_loss_unclipped.mean()
if self.ppo2_vf_coef_halving:
v_loss *= 0.5
entropy_loss = -entropy.mean()
loss = pi_loss + ent_coef * entropy_loss + self.vf_coef * v_loss
self.optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
self.optimizer.step()
with torch.no_grad():
approx_kl = ((ratio - 1) - logratio).mean().cpu().numpy().item()
clipped_frac = (
((ratio - 1).abs() > pi_clip).float().mean().cpu().numpy().item()
)
val_clipped_frac = (
(((v - orig_v).abs() > v_clip).float().mean().cpu().numpy().item())
if v_clip
else 0
)
return TrainStepStats(
loss.item(),
pi_loss.item(),
v_loss.item(),
entropy_loss.item(),
approx_kl,
clipped_frac,
val_clipped_frac,
)
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