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"""PPO agent for Atari (Breakout / Pong).
Schulman et al., 2017: "Proximal Policy Optimization Algorithms"
(arXiv:1707.06347). Same clipped-surrogate + GAE objective as the
cartpole PPO, but with the Nature CNN as the shared trunk and the
DeepMind reward clipping that keeps the value function stable.
Rollout uses 8 parallel envs via SyncVectorEnv (CleanRL convention).
Bump TOTAL_FRAMES well past the default for paper-quality results.
"""
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from env import make_env, make_vec_env, parse_args, pick_device, run_test_loop
SAVE_PATH = "atari_ppo.pt"
TOTAL_FRAMES = 10_000_000
N_ENVS = 8
ROLLOUT_STEPS = 128 # batch = N_ENVS * ROLLOUT_STEPS = 1024
EPOCHS = 4
MINIBATCH_SIZE = 256
CLIP_COEF = 0.1
GAMMA = 0.99
GAE_LAMBDA = 0.95
LR = 2.5e-4
VALUE_COEF = 0.5
ENTROPY_COEF = 0.01
MAX_GRAD_NORM = 0.5
def _ortho(layer, gain):
nn.init.orthogonal_(layer.weight, gain)
nn.init.zeros_(layer.bias)
return layer
# Nature CNN shared trunk + policy and value heads.
class ActorCritic(nn.Module):
def __init__(self, n_actions):
super().__init__()
self.conv = nn.Sequential(
_ortho(nn.Conv2d(4, 32, kernel_size=8, stride=4), 2 ** 0.5), nn.ReLU(),
_ortho(nn.Conv2d(32, 64, kernel_size=4, stride=2), 2 ** 0.5), nn.ReLU(),
_ortho(nn.Conv2d(64, 64, kernel_size=3, stride=1), 2 ** 0.5), nn.ReLU(),
nn.Flatten(),
_ortho(nn.Linear(64 * 7 * 7, 512), 2 ** 0.5), nn.ReLU(),
)
# gain=0.01 keeps the initial action distribution close to uniform.
self.policy = _ortho(nn.Linear(512, n_actions), 0.01)
self.value = _ortho(nn.Linear(512, 1), 1.0)
def forward(self, x):
h = self.conv(x.float() / 255.0)
return self.policy(h), self.value(h).squeeze(-1)
def compute_gae(rewards, values, dones, last_value):
advantages = np.zeros_like(rewards, dtype=np.float32)
gae = 0.0
for t in reversed(range(len(rewards))):
next_v = last_value if t == len(rewards) - 1 else values[t + 1]
next_nonterminal = 1.0 - dones[t]
delta = rewards[t] + GAMMA * next_v * next_nonterminal - values[t]
gae = delta + GAMMA * GAE_LAMBDA * next_nonterminal * gae
advantages[t] = gae
returns = advantages + values
return advantages, returns
if __name__ == "__main__":
args = parse_args()
device = pick_device(args.device)
if args.test:
env = make_env(args)
n_actions = env.action_space.n
model = ActorCritic(n_actions).to(device)
model.load_state_dict(torch.load(SAVE_PATH, map_location=device))
def policy_action(obs):
with torch.no_grad():
t = torch.as_tensor(np.asarray(obs), device=device).unsqueeze(0)
logits, _ = model(t)
return int(torch.distributions.Categorical(logits=logits).sample().item())
run_test_loop(env, policy_action)
envs = make_vec_env(args, N_ENVS)
n_actions = envs.single_action_space.n
obs_shape = envs.single_observation_space.shape # (4, 84, 84)
model = ActorCritic(n_actions).to(device)
optimizer = optim.Adam(model.parameters(), lr=LR, eps=1e-5)
if args.wandb:
import wandb
wandb.init(project="rl-atari-ppo", config={
"env": args.env, "n_envs": N_ENVS, "rollout_steps": ROLLOUT_STEPS,
"total_frames": TOTAL_FRAMES, "epochs": EPOCHS,
"minibatch_size": MINIBATCH_SIZE, "clip_coef": CLIP_COEF,
"gamma": GAMMA, "gae_lambda": GAE_LAMBDA, "lr": LR,
"value_coef": VALUE_COEF, "entropy_coef": ENTROPY_COEF,
})
print(f"device: {device}, env: {args.env}, actions: {n_actions}, n_envs: {N_ENVS}")
batch_size = ROLLOUT_STEPS * N_ENVS
frames_per_update = batch_size
n_updates = TOTAL_FRAMES // frames_per_update
obs, _ = envs.reset()
ep_returns_per_env = np.zeros(N_ENVS, dtype=np.float32) # per-life (resets every life loss)
game_returns_per_env = np.zeros(N_ENVS, dtype=np.float32) # per-game (resets only on real game-over)
ep_returns = []
game_returns = []
for update in range(1, n_updates + 1):
# Linear LR anneal from LR -> 0 over the run (CleanRL convention).
lr_now = LR * (1.0 - (update - 1) / n_updates)
for g in optimizer.param_groups:
g["lr"] = lr_now
obs_buf = np.zeros((ROLLOUT_STEPS, N_ENVS, *obs_shape), dtype=np.uint8)
act_buf = np.zeros((ROLLOUT_STEPS, N_ENVS), dtype=np.int64)
logp_buf = np.zeros((ROLLOUT_STEPS, N_ENVS), dtype=np.float32)
rew_buf = np.zeros((ROLLOUT_STEPS, N_ENVS), dtype=np.float32)
done_buf = np.zeros((ROLLOUT_STEPS, N_ENVS), dtype=np.float32)
val_buf = np.zeros((ROLLOUT_STEPS, N_ENVS), dtype=np.float32)
# --- Rollout ---
for t in range(ROLLOUT_STEPS):
with torch.no_grad():
obs_t = torch.as_tensor(np.asarray(obs), device=device)
logits, value = model(obs_t)
dist = torch.distributions.Categorical(logits=logits)
action = dist.sample()
logp = dist.log_prob(action)
obs_buf[t] = np.asarray(obs)
act_buf[t] = action.cpu().numpy()
logp_buf[t] = logp.cpu().numpy()
val_buf[t] = value.cpu().numpy()
next_obs, reward, terminated, truncated, info = envs.step(act_buf[t])
done = np.logical_or(terminated, truncated)
ep_returns_per_env += reward
game_returns_per_env += reward
rew_buf[t] = np.sign(reward).astype(np.float32) # DeepMind reward clipping
done_buf[t] = done.astype(np.float32)
# LifeLossTerminalEnv tags each step's info with game_over (True only on real game-over).
game_over = info.get("game_over", done)
for i in range(N_ENVS):
if done[i]:
ep_returns.append(float(ep_returns_per_env[i]))
ep_returns_per_env[i] = 0.0
if bool(game_over[i]):
game_returns.append(float(game_returns_per_env[i]))
game_returns_per_env[i] = 0.0
obs = next_obs
# --- GAE ---
with torch.no_grad():
obs_t = torch.as_tensor(np.asarray(obs), device=device)
_, last_value = model(obs_t)
advantages, returns = compute_gae(rew_buf, val_buf, done_buf, last_value.cpu().numpy())
# Flatten (T, N_ENVS, ...) -> (T*N_ENVS, ...)
obs_t = torch.as_tensor(obs_buf.reshape(batch_size, *obs_shape), device=device)
act_t = torch.as_tensor(act_buf.reshape(batch_size), device=device)
old_logp_t = torch.as_tensor(logp_buf.reshape(batch_size), device=device)
old_val_t = torch.as_tensor(val_buf.reshape(batch_size), device=device)
adv_t = torch.as_tensor(advantages.reshape(batch_size), device=device)
ret_t = torch.as_tensor(returns.reshape(batch_size), device=device)
# --- PPO updates ---
idx = np.arange(batch_size)
pl_sum = vl_sum = ent_sum = 0.0
n_mb = 0
for _ in range(EPOCHS):
np.random.shuffle(idx)
for start in range(0, batch_size, MINIBATCH_SIZE):
mb = idx[start:start + MINIBATCH_SIZE]
logits, values = model(obs_t[mb])
dist = torch.distributions.Categorical(logits=logits)
new_logp = dist.log_prob(act_t[mb])
entropy = dist.entropy().mean()
# Advantage normalization per minibatch (CleanRL convention).
mb_adv = adv_t[mb]
mb_adv = (mb_adv - mb_adv.mean()) / (mb_adv.std() + 1e-8)
ratio = (new_logp - old_logp_t[mb]).exp()
unclipped = ratio * mb_adv
clipped = torch.clamp(ratio, 1 - CLIP_COEF, 1 + CLIP_COEF) * mb_adv
policy_loss = -torch.min(unclipped, clipped).mean()
# Value loss with clipping around the old value prediction.
v_clipped = old_val_t[mb] + torch.clamp(
values - old_val_t[mb], -CLIP_COEF, CLIP_COEF)
vl_unclipped = (values - ret_t[mb]).pow(2)
vl_clipped = (v_clipped - ret_t[mb]).pow(2)
value_loss = 0.5 * torch.max(vl_unclipped, vl_clipped).mean()
loss = policy_loss + VALUE_COEF * value_loss - ENTROPY_COEF * entropy
optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), MAX_GRAD_NORM)
optimizer.step()
pl_sum += policy_loss.item()
vl_sum += value_loss.item()
ent_sum += entropy.item()
n_mb += 1
global_step = update * frames_per_update
if ep_returns:
life_mean = float(np.mean(ep_returns[-20:]))
game_mean = float(np.mean(game_returns[-20:])) if game_returns else 0.0
print(f"update: {update:>4} frames: {global_step:>8} "
f"per_life: {life_mean:.1f} per_game: {game_mean:.1f} "
f"lives: {len(ep_returns)} games: {len(game_returns)}")
if args.wandb:
log = {
"global_step": global_step,
"policy_loss": pl_sum / n_mb,
"value_loss": vl_sum / n_mb,
"entropy": ent_sum / n_mb,
"lr": lr_now,
}
if ep_returns:
log["recent_mean_return"] = float(np.mean(ep_returns[-20:]))
if game_returns:
log["recent_mean_game_return"] = float(np.mean(game_returns[-20:]))
wandb.log(log, step=global_step)
torch.save(model.state_dict(), SAVE_PATH)
print(f"Saved trained model to {SAVE_PATH}")
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