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import gym_super_mario_bros
import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from gym_super_mario_bros.actions import COMPLEX_MOVEMENT
from nes_py.wrappers import JoypadSpace
from wrappers import *
device = "cpu"
if torch.cuda.is_available():
device = "cuda"
elif torch.backends.mps.is_available():
device = "mps"
print(f"Using device: {device}")
def make_env():
env = gym_super_mario_bros.make("SuperMarioBros-v0")
env = JoypadSpace(env, COMPLEX_MOVEMENT)
env = wrap_mario(env)
return env
def get_reward(r):
r = np.sign(r) * (np.sqrt(abs(r) + 1) - 1) + 0.001 * r
return r
class ActorCritic(nn.Module):
def __init__(self, n_frame, act_dim):
super().__init__()
self.net = nn.Sequential(
nn.Conv2d(n_frame, 32, 8, 4),
nn.ReLU(),
nn.Conv2d(32, 64, 3, 1),
nn.ReLU(),
)
self.linear = nn.Linear(20736, 512)
self.policy_head = nn.Linear(512, act_dim)
self.value_head = nn.Linear(512, 1)
def forward(self, x):
if x.dim() == 4:
x = x.permute(0, 3, 1, 2)
elif x.dim() == 3:
x = x.permute(2, 0, 1)
x = self.net(x)
x = x.reshape(-1, 20736)
x = torch.relu(self.linear(x))
return self.policy_head(x), self.value_head(x).squeeze(-1)
def act(self, obs):
logits, value = self.forward(obs)
dist = torch.distributions.Categorical(logits=logits)
action = dist.sample()
logprob = dist.log_prob(action)
return action, logprob, value
def compute_gae_batch(rewards, values, dones, gamma=0.99, lam=0.95):
T, N = rewards.shape
advantages = torch.zeros_like(rewards)
gae = torch.zeros(N, device=device)
for t in reversed(range(T)):
not_done = 1.0 - dones[t]
delta = rewards[t] + gamma * values[t + 1] * not_done - values[t]
gae = delta + gamma * lam * not_done * gae
advantages[t] = gae
returns = advantages + values[:-1]
return advantages, returns
def rollout_with_bootstrap(envs, model, rollout_steps, init_obs):
obs = init_obs
obs = torch.tensor(obs, dtype=torch.float32).to(device)
obs_buf, act_buf, rew_buf, done_buf, val_buf, logp_buf = [], [], [], [], [], []
for _ in range(rollout_steps):
obs_buf.append(obs)
with torch.no_grad():
action, logp, value = model.act(obs)
val_buf.append(value)
logp_buf.append(logp)
act_buf.append(action)
actions = action.cpu().numpy()
next_obs, reward, done, infos = envs.step(actions)
reward = [get_reward(r) for r in reward]
# done = np.logical_or(terminated)
rew_buf.append(torch.tensor(reward, dtype=torch.float32).to(device))
done_buf.append(torch.tensor(done, dtype=torch.float32).to(device))
for i, d in enumerate(done):
if d:
print(f"Env {i} done. Resetting. (info: {infos[i]})")
next_obs[i] = envs.envs[i].reset()
obs = torch.tensor(next_obs, dtype=torch.float32).to(device)
max_stage = max([i["stage"] for i in infos])
with torch.no_grad():
_, last_value = model.forward(obs)
obs_buf = torch.stack(obs_buf)
act_buf = torch.stack(act_buf)
rew_buf = torch.stack(rew_buf)
done_buf = torch.stack(done_buf)
val_buf = torch.stack(val_buf)
val_buf = torch.cat([val_buf, last_value.unsqueeze(0)], dim=0)
logp_buf = torch.stack(logp_buf)
adv_buf, ret_buf = compute_gae_batch(rew_buf, val_buf, done_buf)
adv_buf = (adv_buf - adv_buf.mean()) / (adv_buf.std() + 1e-8)
return {
"obs": obs_buf, # [T, N, obs_dim]
"actions": act_buf,
"logprobs": logp_buf,
"advantages": adv_buf,
"returns": ret_buf,
"max_stage": max_stage,
"last_obs": obs,
}
def evaluate_policy(env, model, episodes=5, render=False):
"""
Function to evaluate the learned policy
Args:
env: gym.Env single environment (not vector!)
model: ActorCritic model
episodes: number of episodes to evaluate
render: whether to visualize (if True, display on screen)
Returns:
avg_return: average total reward
"""
model.eval()
total_returns = []
actions = []
stages = []
for ep in range(episodes):
obs = env.reset()
done = False
total_reward = 0
if render:
env.render()
while not done:
obs_tensor = (
torch.tensor(np.array(obs), dtype=torch.float32).unsqueeze(0).to(device)
)
with torch.no_grad():
logits, _ = model(obs_tensor)
dist = torch.distributions.Categorical(logits=logits)
action = dist.probs.argmax(dim=-1).item() # greedy action
actions.append(action)
obs, reward, done, info = env.step(action)
stages.append(info["stage"])
total_reward += reward
total_returns.append(total_reward)
info["action_count"] = Counter(actions)
model.train()
return np.mean(total_returns), info, max(stages)
def train_ppo():
num_env = 8
envs = gym.vector.SyncVectorEnv([lambda: make_env() for _ in range(num_env)])
obs_dim = envs.single_observation_space.shape[-1]
act_dim = envs.single_action_space.n
print(f"{obs_dim=} {act_dim=}")
model = ActorCritic(obs_dim, act_dim).to(device)
# Load model with proper device mapping
try:
# Try to load with current device first
model.load_state_dict(torch.load("mario_1_1.pt", map_location=device))
print("Model loaded successfully with current device mapping")
except:
try:
# If that fails, try loading with CPU and then moving to device
model.load_state_dict(torch.load("mario_1_1.pt", map_location="cpu"))
model = model.to(device)
print("Model loaded successfully with CPU mapping")
except Exception as e:
print(f"Failed to load model: {e}")
print("Starting with fresh model")
optimizer = optim.Adam(model.parameters(), lr=2.5e-4)
rollout_steps = 128
epochs = 4
minibatch_size = 64
clip_eps = 0.2
vf_coef = 0.5
ent_coef = 0.01
eval_env = make_env()
eval_env.reset()
init_obs = envs.reset()
update = 0
while True:
update += 1
batch = rollout_with_bootstrap(envs, model, rollout_steps, init_obs)
init_obs = batch["last_obs"]
T, N = rollout_steps, envs.num_envs
total_size = T * N
obs = batch["obs"].reshape(total_size, *envs.single_observation_space.shape)
act = batch["actions"].reshape(total_size)
logp_old = batch["logprobs"].reshape(total_size)
adv = batch["advantages"].reshape(total_size)
ret = batch["returns"].reshape(total_size)
for _ in range(epochs):
idx = torch.randperm(total_size)
for start in range(0, total_size, minibatch_size):
i = idx[start : start + minibatch_size]
logits, value = model(obs[i])
dist = torch.distributions.Categorical(logits=logits)
logp = dist.log_prob(act[i])
ratio = torch.exp(logp - logp_old[i])
clipped = torch.clamp(ratio, 1 - clip_eps, 1 + clip_eps) * adv[i]
policy_loss = -torch.min(ratio * adv[i], clipped).mean()
value_loss = (ret[i] - value).pow(2).mean()
entropy = dist.entropy().mean()
loss = policy_loss + vf_coef * value_loss - ent_coef * entropy
optimizer.zero_grad()
loss.backward()
optimizer.step()
# logging
avg_return = batch["returns"].mean().item()
max_stage = batch["max_stage"]
print(f"Update {update}: avg return = {avg_return:.2f} {max_stage=}")
# eval and save
if update % 10 == 0:
avg_score, info, eval_max_stage = evaluate_policy(
eval_env, model, episodes=1, render=False
)
print(f"[Eval] Update {update}: avg return = {avg_score:.2f} info: {info}")
if eval_max_stage > 1:
torch.save(model.state_dict(), "mario_1_1_clear.pt")
break
if update > 0 and update % 50 == 0:
torch.save(model.state_dict(), "mario_1_1_ppo.pt")
if __name__ == "__main__":
train_ppo() |