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Self-contained Pixelcopter REINFORCE trainer for Colab.
Reads HF_TOKEN from env, trains, evaluates, early-stops when it clears the
certification threshold (mean_reward - std >= 5), and pushes to the Hub.
Uses the OLD gym API (gym_pygame / PLE). Run headless via SDL dummy driver.
"""
import os
os.environ.setdefault("SDL_VIDEODRIVER", "dummy")
os.environ.setdefault("SDL_AUDIODRIVER", "dummy")
import json
import copy
from collections import deque
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.distributions import Categorical
import imageio
import gym
import gym_pygame # noqa: F401 registers Pixelcopter-PLE-v0
from huggingface_hub import HfApi, login, metadata_eval_result, metadata_save
TOKEN = os.environ["HF_TOKEN"]
USER = "SamTru88"
ENV_ID = "Pixelcopter-PLE-v0"
REPO = f"{USER}/Reinforce-{ENV_ID}"
THRESHOLD = 5.0
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class Policy(nn.Module):
def __init__(self, s, a, h=64):
super().__init__()
self.fc1 = nn.Linear(s, h)
self.fc2 = nn.Linear(h, h * 2)
self.fc3 = nn.Linear(h * 2, a)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
return torch.softmax(self.fc3(x), dim=1)
def act(self, state):
state = torch.from_numpy(np.array(state)).float().unsqueeze(0).to(device)
probs = self.forward(state)
m = Categorical(probs)
action = m.sample()
return action.item(), m.log_prob(action)
def make_env():
return gym.make(ENV_ID)
def evaluate(policy, n_eval, max_t):
env = make_env()
rewards = []
for _ in range(n_eval):
state = env.reset()
total = 0.0
for _ in range(max_t):
a, _ = policy.act(state)
state, r, done, _ = env.step(a)
total += r
if done:
break
rewards.append(total)
return float(np.mean(rewards)), float(np.std(rewards))
def train():
env = make_env()
s_size = env.observation_space.shape[0]
a_size = env.action_space.n
policy = Policy(s_size, a_size, 64).to(device)
opt = optim.Adam(policy.parameters(), lr=1e-4)
gamma, max_t = 0.99, 10000
scores = deque(maxlen=100)
best_score, best_state = -1e9, copy.deepcopy(policy.state_dict())
for ep in range(1, 60001):
log_probs, rewards = [], []
state = env.reset()
for _ in range(max_t):
a, lp = policy.act(state)
log_probs.append(lp)
state, r, done, _ = env.step(a)
rewards.append(r)
if done:
break
scores.append(sum(rewards))
returns = deque()
R = 0.0
for r in reversed(rewards):
R = r + gamma * R
returns.appendleft(R)
returns = torch.tensor(returns)
returns = (returns - returns.mean()) / (returns.std() + 1e-9)
loss = torch.cat([(-lp * R).reshape(1) for lp, R in zip(log_probs, returns)]).sum()
opt.zero_grad(); loss.backward(); opt.step()
# periodic eval + early stop with best checkpoint
if ep % 1000 == 0:
m, sd = evaluate(policy, 10, max_t)
score = m - sd
print(f"ep {ep}\tavg100 {np.mean(scores):.2f}\teval score {score:.2f}", flush=True)
if score > best_score:
best_score = score
best_state = copy.deepcopy(policy.state_dict())
if best_score >= THRESHOLD + 1: # small margin
break
policy.load_state_dict(best_state)
return policy, s_size, a_size
def push(policy, s_size, a_size, mean_r, std_r):
login(token=TOKEN, add_to_git_credential=False)
api = HfApi(token=TOKEN)
api.create_repo(REPO, exist_ok=True, repo_type="model")
os.makedirs("out", exist_ok=True)
torch.save(policy.state_dict(), "out/model.pt")
hp = dict(env_id=ENV_ID, h_size=64, n_training_episodes=60000,
n_evaluation_episodes=10, max_t=10000, gamma=0.99, lr=1e-4,
state_space=int(s_size), action_space=int(a_size))
json.dump(hp, open("out/hyperparameters.json", "w"), indent=2)
# replay video
env = make_env()
imgs = []
state = env.reset()
for _ in range(2000):
a, _ = policy.act(state)
state, _, done, _ = env.step(a)
try:
imgs.append(np.array(env.render(mode="rgb_array")))
except Exception:
pass
if done:
break
if imgs:
imageio.mimsave("out/replay.mp4", imgs, fps=30)
meta = {"tags": [ENV_ID, "reinforce", "reinforcement-learning",
"custom-implementation", "deep-reinforcement-learning"]}
eval_meta = metadata_eval_result(
model_pretty_name=f"Reinforce-{ENV_ID}", task_pretty_name="reinforcement-learning",
task_id="reinforcement-learning", metrics_pretty_name="mean_reward",
metrics_id="mean_reward", metrics_value=f"{mean_r:.2f} +/- {std_r:.2f}",
dataset_pretty_name=ENV_ID, dataset_id=ENV_ID)
meta = {**meta, **eval_meta}
with open("out/README.md", "w", encoding="utf-8") as f:
f.write(f"# REINFORCE agent for {ENV_ID}\n\nMean reward: {mean_r:.2f} +/- {std_r:.2f}\n")
metadata_save("out/README.md", meta)
api.upload_folder(repo_id=REPO, folder_path="out", path_in_repo=".",
commit_message="REINFORCE Pixelcopter (Deep RL Unit 4)")
print(f"PUSHED https://huggingface.co/{REPO}", flush=True)
if __name__ == "__main__":
policy, s_size, a_size = train()
m, sd = evaluate(policy, 10, 10000)
print(f"FINAL mean={m:.2f} std={sd:.2f} score={m-sd:.2f} threshold={THRESHOLD} "
f"{'PASS' if m-sd >= THRESHOLD else 'FAIL'}", flush=True)
if m - sd >= THRESHOLD:
push(policy, s_size, a_size, m, sd)
else:
print("Did not reach threshold; not pushing.", flush=True)
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