File size: 20,789 Bytes
61c9b91 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 |
# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/c51/#c51_ataripy
import argparse
import math
import os
import random
import time
from collections import deque
from distutils.util import strtobool
from types import SimpleNamespace
import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
import torch.optim as optim
from stable_baselines3.common.atari_wrappers import ClipRewardEnv, EpisodicLifeEnv, FireResetEnv, MaxAndSkipEnv, NoopResetEnv
from torch.utils.tensorboard import SummaryWriter
def parse_args():
# fmt: off
parser = argparse.ArgumentParser()
parser.add_argument("--exp-name", type=str, default=os.path.basename(__file__).rstrip(".py"),
help="the name of this experiment")
parser.add_argument("--seed", type=int, default=1,
help="seed of the experiment")
parser.add_argument("--torch-deterministic", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="if toggled, `torch.backends.cudnn.deterministic=False`")
parser.add_argument("--cuda", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="if toggled, cuda will be enabled by default")
parser.add_argument("--track", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
help="if toggled, this experiment will be tracked with Weights and Biases")
parser.add_argument("--wandb-project-name", type=str, default="cleanRL",
help="the wandb's project name")
parser.add_argument("--wandb-entity", type=str, default=None,
help="the entity (team) of wandb's project")
parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
help="whether to capture videos of the agent performances (check out `videos` folder)")
parser.add_argument("--save-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
help="whether to save model into the `runs/{run_name}` folder")
parser.add_argument("--upload-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
help="whether to upload the saved model to huggingface")
parser.add_argument("--hf-entity", type=str, default="",
help="the user or org name of the model repository from the Hugging Face Hub")
# Algorithm specific arguments
parser.add_argument("--env-id", type=str, default="BreakoutNoFrameskip-v4",
help="the id of the environment")
parser.add_argument("--total-timesteps", type=int, default=10000000,
help="total timesteps of the experiments")
parser.add_argument("--learning-rate", type=float, default=2.5e-4,
help="the learning rate of the optimizer")
parser.add_argument("--num-envs", type=int, default=1,
help="the number of parallel game environments")
parser.add_argument("--n-atoms", type=int, default=51,
help="the number of atoms")
parser.add_argument("--v-min", type=float, default=-10,
help="the return lower bound")
parser.add_argument("--v-max", type=float, default=10,
help="the return upper bound")
parser.add_argument("--buffer-size", type=int, default=1000000,
help="the replay memory buffer size")
parser.add_argument("--gamma", type=float, default=0.99,
help="the discount factor gamma")
parser.add_argument("--target-network-frequency", type=int, default=10000,
help="the timesteps it takes to update the target network")
parser.add_argument("--batch-size", type=int, default=32,
help="the batch size of sample from the reply memory")
parser.add_argument("--learning-starts", type=int, default=80000,
help="timestep to start learning")
parser.add_argument("--train-frequency", type=int, default=4,
help="the frequency of training")
args = parser.parse_args()
# fmt: on
assert args.num_envs == 1, "vectorized envs are not supported at the moment"
return args
def make_env(env_id, seed, idx, capture_video, run_name):
def thunk():
if capture_video and idx == 0:
env = gym.make(env_id, render_mode="rgb_array")
env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
else:
env = gym.make(env_id)
env = gym.wrappers.RecordEpisodeStatistics(env)
env = NoopResetEnv(env, noop_max=30)
env = MaxAndSkipEnv(env, skip=4)
env = EpisodicLifeEnv(env)
if "FIRE" in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = ClipRewardEnv(env)
env = gym.wrappers.ResizeObservation(env, (84, 84))
env = gym.wrappers.GrayScaleObservation(env)
env = gym.wrappers.FrameStack(env, 4)
env.action_space.seed(seed)
return env
return thunk
class SumTree:
def __init__(self, capacity):
self.capacity = capacity # Capacity of the sum tree (number of leaves)
self.tree = [0] * (2 * capacity) # Binary tree representation
self.max_priority = 1.0 # Initial max priority for new experiences
def update(self, index, priority=None):
if priority is None:
priority = self.max_priority
tree_idx = index + self.capacity
change = priority - self.tree[tree_idx]
self.tree[tree_idx] = priority
self._propagate(tree_idx, change)
self.max_priority = max(self.max_priority, priority)
def _propagate(self, idx, change):
parent = idx // 2
while parent != 0:
self.tree[parent] += change
parent = parent // 2
def total(self):
return self.tree[1] # The root of the tree holds the total sum
def get(self, s):
idx = 1
while idx < self.capacity: # Keep moving down the tree to find the index
left = 2 * idx
right = left + 1
if self.tree[left] >= s:
idx = left
else:
s -= self.tree[left]
idx = right
return idx - self.capacity
class PrioritizedReplayBuffer:
def __init__(self, size, device, alpha=0.5, beta_0=0.4, n_step=3, gamma=0.99):
self.size = size
self.device = device
self.alpha = alpha
self.beta_0 = beta_0
self.update_beta(0.0)
self.n_step = n_step
self.gamma = gamma
self.next_index = 0
self.sum_tree = SumTree(size)
self.observations = np.zeros((self.size, 4, 84, 84), dtype=np.uint8)
self.next_observations = np.zeros((self.size, 4, 84, 84), dtype=np.uint8)
self.actions = np.zeros((self.size, 1), dtype=np.int64)
self.rewards = np.zeros((self.size, 1), dtype=np.float32)
self.dones = np.zeros((self.size, 1), dtype=bool)
self.n_step_buffer = deque(maxlen=n_step)
def add(self, obs, next_obs, actions, rewards, dones, infos):
self.n_step_buffer.append((obs[0], next_obs[0], actions[0], rewards[0], dones[0], infos))
if len(self.n_step_buffer) < self.n_step and not dones[0]:
return
# Compute n-step return and the first state and action
rewards = [self.n_step_buffer[i][3] for i in range(len(self.n_step_buffer))]
n_step_return = sum([r * (self.gamma**i) for i, r in enumerate(rewards)])
obs, _, action, _, _, _ = self.n_step_buffer[0]
_, next_obs, _, _, done, _ = self.n_step_buffer[-1]
# Store the n-step transition
self.observations[self.next_index] = obs
self.next_observations[self.next_index] = next_obs
self.actions[self.next_index] = action
self.rewards[self.next_index] = n_step_return
self.dones[self.next_index] = done
# Get the max priority in the tree and set the new transition with max priority
self.sum_tree.update(self.next_index)
self.next_index = (self.next_index + 1) % self.size
if dones[0]:
self.n_step_buffer.clear()
def sample(self, batch_size):
segment = self.sum_tree.total() / batch_size
idxs = []
priorities = []
for i in range(batch_size):
a = segment * i
b = segment * (i + 1)
s = random.uniform(a, b)
idx = self.sum_tree.get(s)
idxs.append(idx)
leaf_idx = idx + self.size # Adjusting index to point to the leaf node
priorities.append(self.sum_tree.tree[leaf_idx])
priorities = torch.tensor(priorities, dtype=torch.float32, device=self.device).unsqueeze(1)
sampling_probabilities = priorities / self.sum_tree.total()
weights = (self.size * sampling_probabilities) ** (-self.beta)
weights /= weights.max() # Normalize for stability
data = SimpleNamespace(
observations=torch.from_numpy(self.observations[idxs]).to(self.device),
next_observations=torch.from_numpy(self.next_observations[idxs]).to(self.device),
actions=torch.from_numpy(self.actions[idxs]).to(self.device),
rewards=torch.from_numpy(self.rewards[idxs]).to(self.device),
dones=torch.from_numpy(self.dones[idxs]).to(self.device),
)
return data, idxs, weights
def update_priorities(self, idxs, errors):
for idx, error in zip(idxs, errors):
priority = (abs(error) + 1e-5) ** self.alpha
self.sum_tree.update(idx, priority)
def update_beta(self, fraction):
self.beta = (1.0 - self.beta_0) * fraction + self.beta_0
class NoisyLinear(nn.Module):
def __init__(self, in_features, out_features, std_init=0.1):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.std_init = std_init
self.weight_mu = nn.Parameter(torch.Tensor(out_features, in_features))
self.weight_sigma = nn.Parameter(torch.Tensor(out_features, in_features))
self.register_buffer("weight_epsilon", torch.Tensor(out_features, in_features))
self.bias_mu = nn.Parameter(torch.Tensor(out_features))
self.bias_sigma = nn.Parameter(torch.Tensor(out_features))
self.register_buffer("bias_epsilon", torch.Tensor(out_features))
self.reset_parameters()
self.reset_noise()
def reset_parameters(self):
init.kaiming_uniform_(self.weight_mu, a=math.sqrt(5))
init.constant_(self.weight_sigma, self.std_init / math.sqrt(self.in_features))
init.constant_(self.bias_mu, 0)
init.constant_(self.bias_sigma, self.std_init / math.sqrt(self.out_features))
def reset_noise(self):
epsilon_in = self._scale_noise(self.in_features)
epsilon_out = self._scale_noise(self.out_features)
self.weight_epsilon.copy_(epsilon_out.outer(epsilon_in))
self.bias_epsilon.copy_(epsilon_out)
def _scale_noise(self, size):
x = torch.randn(size, device=self.weight_mu.device)
return x.sign().mul_(x.abs().sqrt_())
def forward(self, input):
weight = self.weight_mu + self.weight_sigma * self.weight_epsilon if self.training else self.weight_mu
bias = self.bias_mu + self.bias_sigma * self.bias_epsilon if self.training else self.bias_mu
return F.linear(input, weight, bias)
# ALGO LOGIC: initialize agent here:
class QNetwork(nn.Module):
def __init__(self, env, n_atoms=101, v_min=-100, v_max=100):
super().__init__()
self.env = env
self.n_atoms = n_atoms
self.register_buffer("atoms", torch.linspace(v_min, v_max, steps=n_atoms))
self.n = env.single_action_space.n
self.shared_layers = nn.Sequential(
nn.Conv2d(4, 32, 8, stride=4),
nn.ReLU(),
nn.Conv2d(32, 64, 4, stride=2),
nn.ReLU(),
nn.Conv2d(64, 64, 3, stride=1),
nn.ReLU(),
nn.Flatten(),
)
self.value_stream = nn.Sequential(NoisyLinear(3136, 512), nn.ReLU(), NoisyLinear(512, n_atoms))
self.advantage_stream = nn.Sequential(NoisyLinear(3136, 512), nn.ReLU(), NoisyLinear(512, self.n * n_atoms))
def reset_noise(self):
for module in self.modules():
if isinstance(module, NoisyLinear):
module.reset_noise()
def get_action(self, obs):
q_values_distributions = self.get_distribution(obs)
q_values = (torch.softmax(q_values_distributions, dim=2) * self.atoms).sum(2)
return torch.argmax(q_values, 1)
def get_distribution(self, obs):
x = self.shared_layers(obs / 255.0)
value = self.value_stream(x).view(-1, 1, self.n_atoms)
advantages = self.advantage_stream(x).view(-1, self.n, self.n_atoms)
return value + (advantages - advantages.mean(dim=1, keepdim=True))
if __name__ == "__main__":
import stable_baselines3 as sb3
if sb3.__version__ < "2.0":
raise ValueError(
"""Ongoing migration: run the following command to install the new dependencies:
poetry run pip install "stable_baselines3==2.0.0a1" "gymnasium[atari,accept-rom-license]==0.28.1" "ale-py==0.8.1"
"""
)
args = parse_args()
run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
if args.track:
import wandb
wandb.init(
project=args.wandb_project_name,
entity=args.wandb_entity,
sync_tensorboard=True,
config=vars(args),
name=run_name,
monitor_gym=True,
save_code=True,
)
writer = SummaryWriter(f"runs/{run_name}")
writer.add_text(
"hyperparameters",
"|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
)
# TRY NOT TO MODIFY: seeding
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.backends.cudnn.deterministic = args.torch_deterministic
device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
# env setup
envs = gym.vector.SyncVectorEnv(
[make_env(args.env_id, args.seed + i, i, args.capture_video, run_name) for i in range(args.num_envs)]
)
assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
q_network = QNetwork(envs, n_atoms=args.n_atoms, v_min=args.v_min, v_max=args.v_max).to(device)
optimizer = optim.Adam(q_network.parameters(), lr=args.learning_rate, eps=0.01 / args.batch_size)
target_network = QNetwork(envs, n_atoms=args.n_atoms, v_min=args.v_min, v_max=args.v_max).to(device)
target_network.load_state_dict(q_network.state_dict())
rb = PrioritizedReplayBuffer(args.buffer_size, device)
start_time = time.time()
# TRY NOT TO MODIFY: start the game
obs, _ = envs.reset(seed=args.seed)
for global_step in range(args.total_timesteps):
# ALGO LOGIC: put action logic here
actions = q_network.get_action(torch.Tensor(obs).to(device))
actions = actions.cpu().numpy()
# TRY NOT TO MODIFY: execute the game and log data.
next_obs, rewards, terminations, truncations, infos = envs.step(actions)
# TRY NOT TO MODIFY: record rewards for plotting purposes
if "final_info" in infos:
for info in infos["final_info"]:
# Skip the envs that are not done
if "episode" not in info:
continue
print(f"global_step={global_step}, episodic_return={info['episode']['r']}")
writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step)
writer.add_scalar("charts/episodic_length", info["episode"]["l"], global_step)
break
# TRY NOT TO MODIFY: save data to reply buffer; handle `final_observation`
real_next_obs = next_obs.copy()
for idx, trunc in enumerate(truncations):
if trunc:
real_next_obs[idx] = infos["final_observation"][idx]
rb.add(obs, real_next_obs, actions, rewards, terminations, infos)
# TRY NOT TO MODIFY: CRUCIAL step easy to overlook
obs = next_obs
# ALGO LOGIC: training.
if global_step > args.learning_starts:
if global_step % args.train_frequency == 0:
data, idxs, weights = rb.sample(args.batch_size)
# Combine observations for a single network call
combined_obs = torch.cat([data.observations, data.next_observations], dim=0)
combined_dist = q_network.get_distribution(combined_obs)
dist, next_dist = combined_dist.split(len(data.observations), dim=0)
with torch.no_grad():
next_q_values = (torch.softmax(next_dist, dim=2) * q_network.atoms).sum(2)
next_actions = torch.argmax(next_q_values, 1)
target_next_dist = target_network.get_distribution(data.next_observations)
next_pmfs = torch.softmax(target_next_dist[torch.arange(len(data.next_observations)), next_actions], dim=1)
next_atoms = data.rewards + args.gamma * target_network.atoms * (1 - data.dones.float())
# projection
delta_z = target_network.atoms[1] - target_network.atoms[0]
tz = next_atoms.clamp(args.v_min, args.v_max)
b = (tz - args.v_min) / delta_z
l = b.floor().clamp(0, args.n_atoms - 1)
u = b.ceil().clamp(0, args.n_atoms - 1)
# (l == u).float() handles the case where bj is exactly an integer
# example bj = 1, then the upper ceiling should be uj= 2, and lj= 1
d_m_l = (u + (l == u).float() - b) * next_pmfs
d_m_u = (b - l) * next_pmfs
target_pmfs = torch.zeros_like(next_pmfs)
for i in range(target_pmfs.size(0)):
target_pmfs[i].index_add_(0, l[i].long(), d_m_l[i])
target_pmfs[i].index_add_(0, u[i].long(), d_m_u[i])
old_pmfs = torch.softmax(dist[torch.arange(len(data.observations)), data.actions.flatten()], dim=1)
expected_old_q = (old_pmfs.detach() * q_network.atoms).sum(-1)
expected_target_q = (target_pmfs * target_network.atoms).sum(-1)
td_error = expected_target_q - expected_old_q
rb.update_priorities(idxs, td_error.abs().cpu().numpy())
rb.update_beta(global_step / args.total_timesteps)
loss = (weights * -(target_pmfs * old_pmfs.clamp(min=1e-5, max=1 - 1e-5).log())).sum(-1).mean()
if global_step % 100 == 0:
writer.add_scalar("losses/loss", loss.item(), global_step)
writer.add_scalar("losses/q_values", expected_old_q.mean().item(), global_step)
print("SPS:", int(global_step / (time.time() - start_time)))
writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
# optimize the model
optimizer.zero_grad()
loss.backward()
optimizer.step()
q_network.reset_noise()
# update target network
if global_step % args.target_network_frequency == 0:
target_network.load_state_dict(q_network.state_dict())
if args.save_model:
model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
model_data = {
"model_weights": q_network.state_dict(),
"args": vars(args),
}
torch.save(model_data, model_path)
print(f"model saved to {model_path}")
from cleanrl_utils.evals.rainbow_eval import evaluate
episodic_returns = evaluate(
model_path,
make_env,
args.env_id,
eval_episodes=10,
run_name=f"{run_name}-eval",
Model=QNetwork,
device=device,
)
for idx, episodic_return in enumerate(episodic_returns):
writer.add_scalar("eval/episodic_return", episodic_return, idx)
if args.upload_model:
from cleanrl_utils.huggingface import push_to_hub
repo_name = f"{args.env_id}-{args.exp_name}-seed{args.seed}"
repo_id = f"{args.hf_entity}/{repo_name}" if args.hf_entity else repo_name
push_to_hub(args, episodic_returns, repo_id, "RAINBOW", f"runs/{run_name}", f"videos/{run_name}-eval")
envs.close()
writer.close()
|