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# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/dqn/#dqnpy
import argparse
import os
import random
import time
from distutils.util import strtobool
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from stable_baselines3.common.buffers import ReplayBuffer
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="CartPole-v1",
help="the id of the environment")
parser.add_argument("--total-timesteps", type=int, default=500000,
help="total timesteps of the experiments")
parser.add_argument("--learning-rate", type=float, default=0.0001,
help="the learning rate of the optimizer")
parser.add_argument("--buffer-size", type=int, default=300000,
help="the replay memory buffer size")
parser.add_argument("--gamma", type=float, default=1.0,
help="the discount factor gamma")
parser.add_argument("--target-tau", type=float, default=1.0,
help="the target network update rate")
parser.add_argument("--policy-tau", type=float, default=1.0,
help="the target network update rate")
parser.add_argument("--target-network-frequency", type=int, default=100,
help="the timesteps it takes to update the target network")
parser.add_argument("--policy-network-frequency", type=int, default=1000,
help="the timesteps it takes to update the policy network")
parser.add_argument("--batch-size", type=int, default=256,
help="the batch size of sample from the reply memory")
parser.add_argument("--start-e", type=float, default=1.0,
help="the starting epsilon for exploration")
parser.add_argument("--end-e", type=float, default=0.1,
help="the ending epsilon for exploration")
parser.add_argument("--exploration-fraction", type=float, default=0.2,
help="the fraction of `total-timesteps` it takes from start-e to go end-e")
parser.add_argument("--learning-starts", type=int, default=1000,
help="timestep to start learning")
parser.add_argument("--train-frequency", type=int, default=1,
help="the frequency of training")
args = parser.parse_args()
# fmt: on
return args
def make_env(env_id, seed, idx, capture_video, run_name):
def thunk():
env = gym.make(env_id)
env = gym.wrappers.RecordEpisodeStatistics(env)
if capture_video:
if idx == 0:
env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
env.seed(seed)
env.action_space.seed(seed)
env.observation_space.seed(seed)
return env
return thunk
# ALGO LOGIC: initialize agent here:
class QNetwork(nn.Module):
def __init__(self, env):
super().__init__()
self.network = nn.Sequential(
nn.Linear(np.array(env.single_observation_space.shape).prod(), 512),
nn.ReLU(),
nn.Linear(512, 128),
nn.ReLU(),
nn.Linear(128, env.single_action_space.n),
)
def forward(self, x):
return self.network(x)
def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
slope = (end_e - start_e) / duration
return max(slope * t + start_e, end_e)
if __name__ == "__main__":
args = parse_args()
run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
if args.track:
import wandb
args.alg_type = os.path.basename(__file__)
wandb_sess = wandb.init(
project=args.wandb_project_name,
entity=args.wandb_entity,
config=vars(args),
save_code=True,
# group='string',
name=run_name,
sync_tensorboard=False,
monitor_gym=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()])),
)
def log_value(name: str, x: float, y: int):
# writer.add_scalar(name, x, y)
wandb.log({name: x, "global_step": y})
# TRY NOT TO MODIFY: seeding
torch.manual_seed(args.seed)
# torch.backends.cudnn.deterministic = args.torch_deterministic
np.random.seed(args.seed)
random.seed(args.seed)
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, 0, args.capture_video, run_name)])
assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
q_network = QNetwork(envs).to(device)
optimizer = optim.RMSprop(q_network.parameters(), lr=args.learning_rate)
target_network = QNetwork(envs).to(device)
policy_network = QNetwork(envs).to(device)
target_network.load_state_dict(q_network.state_dict())
policy_network.load_state_dict(q_network.state_dict())
rb = ReplayBuffer(
args.buffer_size,
envs.single_observation_space,
envs.single_action_space,
device,
# optimize_memory_usage=True,
handle_timeout_termination=True,
)
start_time = time.time()
target_update_counter = 0
policy_update_counter = 0
episode_returns = []
# TRY NOT TO MODIFY: start the game
obs = envs.reset()
for global_step in range(args.total_timesteps):
# ALGO LOGIC: put action logic here
epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)
if random.random() < epsilon:
actions = np.array([envs.single_action_space.sample() for _ in range(envs.num_envs)])
else:
q_values = policy_network(torch.Tensor(obs).to(device))
actions = torch.argmax(q_values, dim=1).cpu().numpy()
# TRY NOT TO MODIFY: execute the game and log data.
next_obs, rewards, dones, infos = envs.step(actions)
# TRY NOT TO MODIFY: record rewards for plotting purposes
for info in infos:
if "episode" in info.keys():
episode_returns.append(info['episode']['r'])
episode_returns = episode_returns[-100:]
print(f"step={global_step}, return={info['episode']['r']}, sps={int(global_step / (time.time() - start_time))}")
log_value("perf/episodic_return", info["episode"]["r"], global_step)
log_value("perf/episodic_return_mean_100", np.mean(episode_returns), global_step)
log_value("perf/episodic_return_std_100", np.std(episode_returns), global_step)
log_value("debug/episodic_length", info["episode"]["l"], global_step)
log_value("ex2/epsilon", epsilon, global_step)
break
# TRY NOT TO MODIFY: save data to reply buffer; handle `terminal_observation`
real_next_obs = next_obs.copy()
for idx, d in enumerate(dones):
if d:
real_next_obs[idx] = infos[idx]["terminal_observation"]
rb.add(obs, real_next_obs, actions, rewards, dones, infos)
# TRY NOT TO MODIFY: CRUCIAL step easy to overlook
obs = next_obs
# ALGO LOGIC: training.
if global_step > args.learning_starts:
# NOTE: Current code does not work with train_frequency != 1
if global_step % args.train_frequency == 0:
data = rb.sample(args.batch_size)
with torch.no_grad():
target_max, _ = target_network(data.next_observations).max(dim=1)
td_target = data.rewards.flatten() + args.gamma * target_max * (1 - data.dones.flatten())
old_val = q_network(data.observations).gather(1, data.actions).squeeze()
loss = F.mse_loss(td_target, old_val)
if global_step % 100 == 0:
prev = old_val.detach().cpu().numpy()
new = td_target.detach().cpu().numpy()
diff, a_diff = new-prev, np.abs(new-prev)
mean, a_mean = np.mean(diff), np.mean(a_diff)
median, a_median = np.median(diff), np.median(a_diff)
maximum, a_maximum = np.max(diff), np.max(a_diff)
minimum, a_minimum = np.min(diff), np.min(a_diff)
std, a_std = np.std(diff), np.std(a_diff)
below, a_below = mean - std, a_mean - a_std
above, a_above = mean + std, a_mean + a_std
pu_scalar, a_pu_scalar = 2 * mean / maximum, 2 * a_mean / a_maximum
policy_frequency_scalar_ratio = args.policy_network_frequency * pu_scalar
a_policy_frequency_scalar_ratio = args.policy_network_frequency * a_pu_scalar
log_value("losses/td_loss", loss, global_step)
log_value("losses/q_values", old_val.mean().item(), global_step)
log_value("td/mean", mean, global_step)
log_value("td/a_mean", a_mean, global_step)
log_value("td/median", median, global_step)
log_value("td/a_median", a_median, global_step)
log_value("td/max", maximum, global_step)
log_value("td/a_max", a_maximum, global_step)
log_value("td/min", minimum, global_step)
log_value("td/a_min", a_minimum, global_step)
log_value("td/std", std, global_step)
log_value("td/a_std", a_std, global_step)
log_value("td/below", below, global_step)
log_value("td/a_below", a_below, global_step)
log_value("td/above", above, global_step)
log_value("td/a_above", a_above, global_step)
log_value("alg/pu_scalar", pu_scalar, global_step)
log_value("alg/a_pu_scalar", a_pu_scalar, global_step)
log_value("alg/policy_frequency_scalar_ratio", policy_frequency_scalar_ratio, global_step)
log_value("alg/a_policy_frequency_scalar_ratio", a_policy_frequency_scalar_ratio, global_step)
log_value("debug/steps_per_second", int(global_step / (time.time() - start_time)), global_step)
# optimize the model
optimizer.zero_grad()
loss.backward()
optimizer.step()
# update target network
if global_step % args.target_network_frequency == 0:
target_update_counter += 1
for target_network_param, q_network_param in zip(target_network.parameters(), q_network.parameters()):
target_network_param.data.copy_(
args.target_tau * q_network_param.data + (1.0 - args.target_tau) * target_network_param.data
)
# update policy network
if global_step % args.policy_network_frequency == 0:
policy_update_counter += 1
for policy_network_param, q_network_param in zip(policy_network.parameters(), q_network.parameters()):
policy_network_param.data.copy_(
args.policy_tau * q_network_param.data + (1.0 - args.policy_tau) * policy_network_param.data
)
# for target_network_param, q_network_param in zip(target_network.parameters(), q_network.parameters()):
# target_network_param.data.copy_(
# args.target_tau * q_network_param.data + (1.0 - args.target_tau) * target_network_param.data
# )
if global_step % 100 == 0:
log_value("alg/n_target_update", target_update_counter, global_step)
log_value("alg/n_policy_update", policy_update_counter, global_step)
if args.save_model:
model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
torch.save(policy_network.state_dict(), model_path)
print(f"model saved to {model_path}")
from cleanrl_utils.evals.dqn_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,
epsilon=0.05,
)
for idx, episodic_return in enumerate(episodic_returns):
log_value("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, np.mean(episode_returns), repo_id, "DQPN_freq", f"runs/{run_name}", f"videos/{run_name}-eval")
wandb_sess.finish()
envs.close()
writer.close()
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