ppo_atari_multigpu.py

# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_atari_multigpupy
import os
import random
import time
import warnings
from dataclasses import dataclass, field
from typing import List, Literal

import gymnasium as gym
import numpy as np
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.optim as optim
import tyro
from rich.pretty import pprint
from torch.distributions.categorical import Categorical
from torch.utils.tensorboard import SummaryWriter

from cleanrl_utils.atari_wrappers import (  # isort:skip
    ClipRewardEnv,
    EpisodicLifeEnv,
    FireResetEnv,
    MaxAndSkipEnv,
    NoopResetEnv,
)


@dataclass
class Args:
    exp_name: str = os.path.basename(__file__)[: -len(".py")]
    """the name of this experiment"""
    seed: int = 1
    """seed of the experiment"""
    torch_deterministic: bool = True
    """if toggled, `torch.backends.cudnn.deterministic=False`"""
    cuda: bool = True
    """if toggled, cuda will be enabled by default"""
    track: bool = False
    """if toggled, this experiment will be tracked with Weights and Biases"""
    wandb_project_name: str = "cleanRL"
    """the wandb's project name"""
    wandb_entity: str = None
    """the entity (team) of wandb's project"""
    capture_video: bool = False
    """whether to capture videos of the agent performances (check out `videos` folder)"""

    # Algorithm specific arguments
    env_id: str = "BreakoutNoFrameskip-v4"
    """the id of the environment"""
    total_timesteps: int = 10000000
    """total timesteps of the experiments"""
    learning_rate: float = 2.5e-4
    """the learning rate of the optimizer"""
    local_num_envs: int = 8
    """the number of parallel game environments (in the local rank)"""
    num_steps: int = 128
    """the number of steps to run in each environment per policy rollout"""
    anneal_lr: bool = True
    """Toggle learning rate annealing for policy and value networks"""
    gamma: float = 0.99
    """the discount factor gamma"""
    gae_lambda: float = 0.95
    """the lambda for the general advantage estimation"""
    num_minibatches: int = 4
    """the number of mini-batches"""
    update_epochs: int = 4
    """the K epochs to update the policy"""
    norm_adv: bool = True
    """Toggles advantages normalization"""
    clip_coef: float = 0.1
    """the surrogate clipping coefficient"""
    clip_vloss: bool = True
    """Toggles whether or not to use a clipped loss for the value function, as per the paper."""
    ent_coef: float = 0.01
    """coefficient of the entropy"""
    vf_coef: float = 0.5
    """coefficient of the value function"""
    max_grad_norm: float = 0.5
    """the maximum norm for the gradient clipping"""
    target_kl: float = None
    """the target KL divergence threshold"""
    device_ids: List[int] = field(default_factory=lambda: [])
    """the device ids that subprocess workers will use"""
    backend: Literal["gloo", "nccl", "mpi"] = "gloo"
    """the backend for distributed training"""

    # to be filled in runtime
    local_batch_size: int = 0
    """the local batch size in the local rank (computed in runtime)"""
    local_minibatch_size: int = 0
    """the local mini-batch size in the local rank (computed in runtime)"""
    num_envs: int = 0
    """the number of parallel game environments (computed in runtime)"""
    batch_size: int = 0
    """the batch size (computed in runtime)"""
    minibatch_size: int = 0
    """the mini-batch size (computed in runtime)"""
    num_iterations: int = 0
    """the number of iterations (computed in runtime)"""
    world_size: int = 0
    """the number of processes (computed in runtime)"""


def make_env(env_id, 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)
        return env

    return thunk


def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
    torch.nn.init.orthogonal_(layer.weight, std)
    torch.nn.init.constant_(layer.bias, bias_const)
    return layer


class Agent(nn.Module):
    def __init__(self, envs):
        super().__init__()
        self.network = nn.Sequential(
            layer_init(nn.Conv2d(4, 32, 8, stride=4)),
            nn.ReLU(),
            layer_init(nn.Conv2d(32, 64, 4, stride=2)),
            nn.ReLU(),
            layer_init(nn.Conv2d(64, 64, 3, stride=1)),
            nn.ReLU(),
            nn.Flatten(),
            layer_init(nn.Linear(64 * 7 * 7, 512)),
            nn.ReLU(),
        )
        self.actor = layer_init(nn.Linear(512, envs.single_action_space.n), std=0.01)
        self.critic = layer_init(nn.Linear(512, 1), std=1)

    def get_value(self, x):
        return self.critic(self.network(x / 255.0))

    def get_action_and_value(self, x, action=None):
        hidden = self.network(x / 255.0)
        logits = self.actor(hidden)
        probs = Categorical(logits=logits)
        if action is None:
            action = probs.sample()
        return action, probs.log_prob(action), probs.entropy(), self.critic(hidden)


if __name__ == "__main__":
    # torchrun --standalone --nnodes=1 --nproc_per_node=2 ppo_atari_multigpu.py
    # taken from https://pytorch.org/docs/stable/elastic/run.html
    args = tyro.cli(Args)
    local_rank = int(os.getenv("LOCAL_RANK", "0"))
    args.world_size = int(os.getenv("WORLD_SIZE", "1"))
    args.local_batch_size = int(args.local_num_envs * args.num_steps)
    args.local_minibatch_size = int(args.local_batch_size // args.num_minibatches)
    args.num_envs = args.local_num_envs * args.world_size
    args.batch_size = int(args.num_envs * args.num_steps)
    args.minibatch_size = int(args.batch_size // args.num_minibatches)
    args.num_iterations = args.total_timesteps // args.batch_size
    if args.world_size > 1:
        dist.init_process_group(args.backend, rank=local_rank, world_size=args.world_size)
    else:
        warnings.warn(
            """
Not using distributed mode!
If you want to use distributed mode, please execute this script with 'torchrun'.
E.g., `torchrun --standalone --nnodes=1 --nproc_per_node=2 ppo_atari_multigpu.py`
        """
        )
    run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
    writer = None
    if local_rank == 0:
        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()])),
        )
        pprint(args)

    # TRY NOT TO MODIFY: seeding
    # CRUCIAL: note that we needed to pass a different seed for each data parallelism worker
    args.seed += local_rank
    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed - local_rank)
    torch.backends.cudnn.deterministic = args.torch_deterministic

    if len(args.device_ids) > 0:
        assert len(args.device_ids) == args.world_size, "you must specify the same number of device ids as `--nproc_per_node`"
        device = torch.device(f"cuda:{args.device_ids[local_rank]}" if torch.cuda.is_available() and args.cuda else "cpu")
    else:
        device_count = torch.cuda.device_count()
        if device_count < args.world_size:
            device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
        else:
            device = torch.device(f"cuda:{local_rank}" if torch.cuda.is_available() and args.cuda else "cpu")

    # env setup
    envs = gym.vector.SyncVectorEnv(
        [make_env(args.env_id, i, args.capture_video, run_name) for i in range(args.local_num_envs)],
    )
    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"

    agent = Agent(envs).to(device)
    torch.manual_seed(args.seed)
    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)

    # ALGO Logic: Storage setup
    obs = torch.zeros((args.num_steps, args.local_num_envs) + envs.single_observation_space.shape).to(device)
    actions = torch.zeros((args.num_steps, args.local_num_envs) + envs.single_action_space.shape).to(device)
    logprobs = torch.zeros((args.num_steps, args.local_num_envs)).to(device)
    rewards = torch.zeros((args.num_steps, args.local_num_envs)).to(device)
    dones = torch.zeros((args.num_steps, args.local_num_envs)).to(device)
    values = torch.zeros((args.num_steps, args.local_num_envs)).to(device)

    # TRY NOT TO MODIFY: start the game
    global_step = 0
    start_time = time.time()
    next_obs, _ = envs.reset(seed=args.seed)
    next_obs = torch.Tensor(next_obs).to(device)
    next_done = torch.zeros(args.local_num_envs).to(device)

    for iteration in range(1, args.num_iterations + 1):
        # Annealing the rate if instructed to do so.
        if args.anneal_lr:
            frac = 1.0 - (iteration - 1.0) / args.num_iterations
            lrnow = frac * args.learning_rate
            optimizer.param_groups[0]["lr"] = lrnow

        for step in range(0, args.num_steps):
            global_step += args.num_envs
            obs[step] = next_obs
            dones[step] = next_done

            # ALGO LOGIC: action logic
            with torch.no_grad():
                action, logprob, _, value = agent.get_action_and_value(next_obs)
                values[step] = value.flatten()
            actions[step] = action
            logprobs[step] = logprob

            # TRY NOT TO MODIFY: execute the game and log data.
            next_obs, reward, terminations, truncations, infos = envs.step(action.cpu().numpy())
            next_done = np.logical_or(terminations, truncations)
            rewards[step] = torch.tensor(reward).to(device).view(-1)
            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(next_done).to(device)

            if not writer:
                continue

            if "final_info" in infos:
                for info in infos["final_info"]:
                    if info and "episode" in info:
                        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)

        print(
            f"local_rank: {local_rank}, action.sum(): {action.sum()}, iteration: {iteration}, agent.actor.weight.sum(): {agent.actor.weight.sum()}"
        )
        # bootstrap value if not done
        with torch.no_grad():
            next_value = agent.get_value(next_obs).reshape(1, -1)
            advantages = torch.zeros_like(rewards).to(device)
            lastgaelam = 0
            for t in reversed(range(args.num_steps)):
                if t == args.num_steps - 1:
                    nextnonterminal = 1.0 - next_done
                    nextvalues = next_value
                else:
                    nextnonterminal = 1.0 - dones[t + 1]
                    nextvalues = values[t + 1]
                delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
                advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
            returns = advantages + values

        # flatten the batch
        b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
        b_logprobs = logprobs.reshape(-1)
        b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
        b_advantages = advantages.reshape(-1)
        b_returns = returns.reshape(-1)
        b_values = values.reshape(-1)

        # Optimizing the policy and value network
        b_inds = np.arange(args.local_batch_size)
        clipfracs = []
        for epoch in range(args.update_epochs):
            np.random.shuffle(b_inds)
            for start in range(0, args.local_batch_size, args.local_minibatch_size):
                end = start + args.local_minibatch_size
                mb_inds = b_inds[start:end]

                _, newlogprob, entropy, newvalue = agent.get_action_and_value(b_obs[mb_inds], b_actions.long()[mb_inds])
                logratio = newlogprob - b_logprobs[mb_inds]
                ratio = logratio.exp()

                with torch.no_grad():
                    # calculate approx_kl http://joschu.net/blog/kl-approx.html
                    old_approx_kl = (-logratio).mean()
                    approx_kl = ((ratio - 1) - logratio).mean()
                    clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()]

                mb_advantages = b_advantages[mb_inds]
                if args.norm_adv:
                    mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)

                # Policy loss
                pg_loss1 = -mb_advantages * ratio
                pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1 + args.clip_coef)
                pg_loss = torch.max(pg_loss1, pg_loss2).mean()

                # Value loss
                newvalue = newvalue.view(-1)
                if args.clip_vloss:
                    v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
                    v_clipped = b_values[mb_inds] + torch.clamp(
                        newvalue - b_values[mb_inds],
                        -args.clip_coef,
                        args.clip_coef,
                    )
                    v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
                    v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
                    v_loss = 0.5 * v_loss_max.mean()
                else:
                    v_loss = 0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean()

                entropy_loss = entropy.mean()
                loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef

                optimizer.zero_grad()
                loss.backward()

                if args.world_size > 1:
                    # batch allreduce ops: see https://github.com/entity-neural-network/incubator/pull/220
                    all_grads_list = []
                    for param in agent.parameters():
                        if param.grad is not None:
                            all_grads_list.append(param.grad.view(-1))
                    all_grads = torch.cat(all_grads_list)
                    dist.all_reduce(all_grads, op=dist.ReduceOp.SUM)
                    offset = 0
                    for param in agent.parameters():
                        if param.grad is not None:
                            param.grad.data.copy_(
                                all_grads[offset : offset + param.numel()].view_as(param.grad.data) / args.world_size
                            )
                            offset += param.numel()

                nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
                optimizer.step()

            if args.target_kl is not None and approx_kl > args.target_kl:
                break

        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
        var_y = np.var(y_true)
        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y

        # TRY NOT TO MODIFY: record rewards for plotting purposes
        if local_rank == 0:
            writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
            writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
            writer.add_scalar("losses/policy_loss", pg_loss.item(), global_step)
            writer.add_scalar("losses/entropy", entropy_loss.item(), global_step)
            writer.add_scalar("losses/old_approx_kl", old_approx_kl.item(), global_step)
            writer.add_scalar("losses/approx_kl", approx_kl.item(), global_step)
            writer.add_scalar("losses/clipfrac", np.mean(clipfracs), global_step)
            writer.add_scalar("losses/explained_variance", explained_var, 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)

    envs.close()
    if local_rank == 0:
        writer.close()
        if args.track:
            wandb.finish()