Add multi-node

scripts/ppo_sp2.py

+import os
+import random
+import time
+from collections import deque
+from dataclasses import dataclass
+from typing import Literal, Optional
+
+
+import ygoenv
+import numpy as np
+import optree
+import tyro
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.distributions import Categorical
+import torch.distributed as dist
+from torch.cuda.amp import GradScaler, autocast
+
+from ygoai.utils import init_ygopro
+from ygoai.rl.utils import RecordEpisodeStatistics
+from ygoai.rl.agent import PPOAgent as Agent
+from ygoai.rl.dist import reduce_gradidents, torchrun_setup, fprint
+from ygoai.rl.buffer import create_obs
+
+
+@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 = False
+    """if toggled, `torch.backends.cudnn.deterministic=False`"""
+    cuda: bool = True
+    """if toggled, cuda will be enabled by default"""
+
+    # Algorithm specific arguments
+    env_id: str = "YGOPro-v0"
+    """the id of the environment"""
+    deck: str = "../assets/deck"
+    """the deck file to use"""
+    deck1: Optional[str] = None
+    """the deck file for the first player"""
+    deck2: Optional[str] = None
+    """the deck file for the second player"""
+    code_list_file: str = "code_list.txt"
+    """the code list file for card embeddings"""
+    embedding_file: Optional[str] = None
+    """the embedding file for card embeddings"""
+    max_options: int = 24
+    """the maximum number of options"""
+    n_history_actions: int = 16
+    """the number of history actions to use"""
+    play_mode: str = "bot"
+    """the play mode, can be combination of 'bot' (greedy), 'random', like 'bot+random'"""
+
+    num_layers: int = 2
+    """the number of layers for the agent"""
+    num_channels: int = 128
+    """the number of channels for the agent"""
+    checkpoint: Optional[str] = None
+    """the checkpoint to load the model from"""
+
+    total_timesteps: int = 2000000000
+    """total timesteps of the experiments"""
+    learning_rate: float = 2.5e-4
+    """the learning rate of the optimizer"""
+    num_envs: int = 8
+    """the number of parallel game environments"""
+    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 = 0.997
+    """the discount factor gamma"""
+    gae_lambda: float = 0.95
+    """the lambda for the general advantage estimation"""
+
+    minibatch_size: int = 256
+    """the mini-batch size"""
+    update_epochs: int = 2
+    """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: Optional[float] = None
+    """the target KL divergence threshold"""
+    backend: Literal["gloo", "nccl", "mpi"] = "nccl"
+    """the backend for distributed training"""
+
+    compile: Optional[str] = None
+    """Compile mode of torch.compile, None for no compilation"""
+    torch_threads: Optional[int] = None
+    """the number of threads to use for torch, defaults to ($OMP_NUM_THREADS or 2) * world_size"""
+    env_threads: Optional[int] = None
+    """the number of threads to use for envpool, defaults to `num_envs`"""
+    fp16_train: bool = False
+    """if toggled, training will be done in fp16 precision"""
+    fp16_eval: bool = False
+    """if toggled, evaluation will be done in fp16 precision"""
+
+    tb_dir: str = "./runs"
+    """tensorboard log directory"""
+    ckpt_dir: str = "./checkpoints"
+    """checkpoint directory"""
+    save_interval: int = 500
+    """the number of iterations to save the model"""
+    log_p: float = 1.0
+    """the probability of logging"""
+    eval_episodes: int = 128
+    """the number of episodes to evaluate the model"""
+    eval_interval: int = 10
+    """the number of iterations to evaluate the model"""
+
+    # 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)"""
+    local_num_envs: int = 0
+    """the number of parallel game environments (in the local rank, computed in runtime)"""
+    batch_size: int = 0
+    """the 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 main():
+    rank = int(os.environ.get("RANK", 0))
+    local_rank = int(os.environ.get("LOCAL_RANK", 0))
+    world_size = int(os.environ.get("WORLD_SIZE", 1))
+    print(f"rank={rank}, local_rank={local_rank}, world_size={world_size}")
+
+    args = tyro.cli(Args)
+    args.world_size = world_size
+    args.local_num_envs = args.num_envs // args.world_size
+    args.local_batch_size = int(args.local_num_envs * args.num_steps)
+    args.local_minibatch_size = int(args.minibatch_size // args.world_size)
+    args.batch_size = int(args.num_envs * args.num_steps)
+    args.num_iterations = args.total_timesteps // args.batch_size
+    args.env_threads = args.env_threads or args.num_envs
+    args.torch_threads = args.torch_threads or (int(os.getenv("OMP_NUM_THREADS", "2")) * args.world_size)
+
+    local_torch_threads = args.torch_threads // args.world_size
+    local_env_threads = args.env_threads // args.world_size
+
+    torch.set_num_threads(local_torch_threads)
+    torch.set_float32_matmul_precision('high')
+
+    if args.world_size > 1:
+        torchrun_setup(args.backend, local_rank)
+
+    timestamp = int(time.time())
+    run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{timestamp}"
+    writer = None
+    if rank == 0:
+        from torch.utils.tensorboard import SummaryWriter
+        writer = SummaryWriter(os.path.join(args.tb_dir, run_name))
+        writer.add_text(
+            "hyperparameters",
+            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
+        )
+
+        ckpt_dir = os.path.join(args.ckpt_dir, run_name)
+        os.makedirs(ckpt_dir, exist_ok=True)
+
+
+    # TRY NOT TO MODIFY: seeding
+    # CRUCIAL: note that we needed to pass a different seed for each data parallelism worker
+    args.seed += rank
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed - rank)
+    if args.torch_deterministic:
+        torch.backends.cudnn.deterministic = True
+    else:
+        torch.backends.cudnn.benchmark = True
+
+    device = torch.device(f"cuda:{local_rank}" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    deck = init_ygopro(args.env_id, "english", args.deck, args.code_list_file)
+    args.deck1 = args.deck1 or deck
+    args.deck2 = args.deck2 or deck
+
+    # env setup
+    envs = ygoenv.make(
+        task_id=args.env_id,
+        env_type="gymnasium",
+        num_envs=args.local_num_envs,
+        num_threads=local_env_threads,
+        seed=args.seed,
+        deck1=args.deck1,
+        deck2=args.deck2,
+        max_options=args.max_options,
+        n_history_actions=args.n_history_actions,
+        play_mode='self',
+    )
+    envs.num_envs = args.local_num_envs
+    obs_space = envs.observation_space
+    action_shape = envs.action_space.shape
+    if local_rank == 0:
+        fprint(f"obs_space={obs_space}, action_shape={action_shape}")
+
+    envs_per_thread = args.local_num_envs // local_env_threads
+    local_eval_episodes = args.eval_episodes // args.world_size
+    local_eval_num_envs = local_eval_episodes
+    eval_envs = ygoenv.make(
+        task_id=args.env_id,
+        env_type="gymnasium",
+        num_envs=local_eval_num_envs,
+        num_threads=max(1, local_eval_num_envs // envs_per_thread),
+        seed=args.seed,
+        deck1=args.deck1,
+        deck2=args.deck2,
+        max_options=args.max_options,
+        n_history_actions=args.n_history_actions,
+        play_mode=args.play_mode,
+    )
+    eval_envs.num_envs = local_eval_num_envs
+
+    envs = RecordEpisodeStatistics(envs)
+    eval_envs = RecordEpisodeStatistics(eval_envs)
+
+    if args.embedding_file:
+        embeddings = np.load(args.embedding_file)
+        embedding_shape = embeddings.shape
+    else:
+        embedding_shape = None
+    L = args.num_layers
+    agent = Agent(args.num_channels, L, L, 1, embedding_shape).to(device)
+
+    if args.checkpoint:
+        agent.load_state_dict(torch.load(args.checkpoint, map_location=device))
+        fprint(f"Loaded checkpoint from {args.checkpoint}")
+    elif args.embedding_file:
+        agent.load_embeddings(embeddings)
+        fprint(f"Loaded embeddings from {args.embedding_file}")
+    if args.embedding_file:
+        agent.freeze_embeddings()
+
+    optim_params = list(agent.parameters())
+    optimizer = optim.Adam(optim_params, lr=args.learning_rate, eps=1e-5)
+
+    scaler = GradScaler(enabled=args.fp16_train, init_scale=2 ** 8)
+
+    def masked_mean(x, valid):
+        x = x.masked_fill(~valid, 0)
+        return x.sum() / valid.float().sum()
+
+    def masked_normalize(x, valid, eps=1e-8):
+        x = x.masked_fill(~valid, 0)
+        n = valid.float().sum()
+        mean = x.sum() / n
+        var = ((x - mean) ** 2).sum() / n
+        std = (var + eps).sqrt()
+        return (x - mean) / std
+
+    def train_step(agent: Agent, scaler, mb_obs, mb_actions, mb_logprobs, mb_advantages, mb_returns, mb_values, mb_learns):
+        with autocast(enabled=args.fp16_train):
+            logits, newvalue, valid = agent(mb_obs)
+            probs = Categorical(logits=logits)
+            newlogprob = probs.log_prob(mb_actions)
+            entropy = probs.entropy()
+        logratio = newlogprob - mb_logprobs
+        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()
+            clipfrac = ((ratio - 1.0).abs() > args.clip_coef).float().mean()
+
+        if args.norm_adv:
+            mb_advantages = masked_normalize(mb_advantages, valid, eps=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)
+        pg_loss = masked_mean(pg_loss, valid)
+
+        # Value loss
+        newvalue = newvalue.view(-1)
+        if args.clip_vloss:
+            v_loss_unclipped = (newvalue - mb_returns) ** 2
+            v_clipped = mb_values + torch.clamp(
+                newvalue - mb_values,
+                -args.clip_coef,
+                args.clip_coef,
+            )
+            v_loss_clipped = (v_clipped - mb_returns) ** 2
+            v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
+            v_loss = 0.5 * v_loss_max
+        else:
+            v_loss = 0.5 * ((newvalue - mb_returns) ** 2)
+        v_loss = masked_mean(v_loss, valid)
+
+        entropy_loss = masked_mean(entropy, valid)
+        loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
+        optimizer.zero_grad()
+        scaler.scale(loss).backward()
+        scaler.unscale_(optimizer)
+        return old_approx_kl, approx_kl, clipfrac, pg_loss, v_loss, entropy_loss
+
+    def predict_step(agent: Agent, next_obs):
+        with torch.no_grad():
+            with autocast(enabled=args.fp16_eval):
+                logits, value, valid = agent(next_obs)
+        return logits, value
+
+    if args.compile:
+        # It seems that using torch.compile twice cause segfault at start, so we use torch.jit.trace here
+        # predict_step = torch.compile(predict_step, mode=args.compile)
+        obs = create_obs(envs.observation_space, (args.local_num_envs,), device=device)
+        with torch.no_grad():
+            traced_model = torch.jit.trace(agent, (obs,), check_tolerance=False, check_trace=False)
+
+        train_step = torch.compile(train_step, mode=args.compile)
+
+    def to_tensor(x, dtype=torch.float32):
+        return optree.tree_map(lambda x: torch.from_numpy(x).to(device=device, dtype=dtype, non_blocking=True), x)
+
+    # ALGO Logic: Storage setup
+    obs = create_obs(obs_space, (args.num_steps, args.local_num_envs), device)
+    actions = torch.zeros((args.num_steps, args.local_num_envs) + action_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), dtype=torch.bool).to(device)
+    values = torch.zeros((args.num_steps, args.local_num_envs)).to(device)
+    learns = torch.zeros((args.num_steps, args.local_num_envs), dtype=torch.bool).to(device)
+    avg_ep_returns = deque(maxlen=1000)
+    avg_win_rates = deque(maxlen=1000)
+
+    # TRY NOT TO MODIFY: start the game
+    global_step = 0
+    warmup_steps = 0
+    start_time = time.time()
+    next_obs, info = envs.reset()
+    next_obs = to_tensor(next_obs, dtype=torch.uint8)
+    next_to_play_ = info["to_play"]
+    next_to_play = to_tensor(next_to_play_)
+    next_done = torch.zeros(args.local_num_envs, device=device, dtype=torch.bool)
+    ai_player1_ = np.concatenate([
+        np.zeros(args.local_num_envs // 2, dtype=np.int64),
+        np.ones(args.local_num_envs // 2, dtype=np.int64)
+    ])
+    np.random.shuffle(ai_player1_)
+    ai_player1 = to_tensor(ai_player1_, dtype=next_to_play.dtype)
+    next_value1 = 0
+    next_value2 = 0
+
+    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
+
+        model_time = 0
+        env_time = 0
+        collect_start = time.time()
+        agent.eval()
+        for step in range(0, args.num_steps):
+            global_step += args.num_envs
+
+            for key in obs:
+                obs[key][step] = next_obs[key]
+            dones[step] = next_done
+            learn = next_to_play == ai_player1
+            learns[step] = learn
+
+            _start = time.time()
+            logits, value = predict_step(traced_model, next_obs)
+            value = value.flatten()
+            probs = Categorical(logits=logits)
+            action = probs.sample()
+            logprob = probs.log_prob(action)
+
+            values[step] = value
+            actions[step] = action
+            logprobs[step] = logprob
+            action = action.cpu().numpy()
+            model_time += time.time() - _start
+
+            next_nonterminal = 1 - next_done.float()
+            next_value1 = torch.where(learn, value, next_value1) * next_nonterminal
+            next_value2 = torch.where(learn, next_value2, value) * next_nonterminal
+
+            _start = time.time()
+            to_play = next_to_play_
+            next_obs, reward, next_done_, info = envs.step(action)
+            next_to_play_ = info["to_play"]
+            next_to_play = to_tensor(next_to_play_)
+            env_time += time.time() - _start
+            rewards[step] = to_tensor(reward)
+            next_obs, next_done = to_tensor(next_obs, torch.uint8), to_tensor(next_done_, torch.bool)
+
+            if not writer:
+                continue
+
+            for idx, d in enumerate(next_done_):
+                if d:
+                    pl = 1 if to_play[idx] == ai_player1_[idx] else -1
+                    episode_length = info['l'][idx]
+                    episode_reward = info['r'][idx] * pl
+                    win = 1 if episode_reward > 0 else 0
+                    avg_ep_returns.append(episode_reward)
+                    avg_win_rates.append(win)
+
+                    if random.random() < args.log_p:
+                        n = 100
+                        if random.random() < 10/n or iteration <= 2:
+                            writer.add_scalar("charts/episodic_return", info["r"][idx], global_step)
+                            writer.add_scalar("charts/episodic_length", info["l"][idx], global_step)
+                            fprint(f"global_step={global_step}, e_ret={episode_reward}, e_len={episode_length}")
+
+                        if random.random() < 1/n:
+                            writer.add_scalar("charts/avg_ep_return", np.mean(avg_ep_returns), global_step)
+                            writer.add_scalar("charts/avg_win_rate", np.mean(avg_win_rates), global_step)
+
+        collect_time = time.time() - collect_start
+        if local_rank == 0:
+            fprint(f"collect_time={collect_time:.4f}, model_time={model_time:.4f}, env_time={env_time:.4f}")
+
+        _start = time.time()
+        # bootstrap value if not done
+        with torch.no_grad():
+            # value = agent.get_value(next_obs).reshape(-1)
+            value = traced_model(next_obs)[1].reshape(-1)
+        advantages = torch.zeros_like(rewards).to(device)
+        nextvalues1 = torch.where(next_to_play == ai_player1, value, next_value1)
+        nextvalues2 = torch.where(next_to_play != ai_player1, value, next_value2)
+        # TODO: optimize this
+        done_used1 = torch.ones_like(next_done, dtype=torch.bool)
+        done_used2 = torch.ones_like(next_done, dtype=torch.bool)
+        reward1 = reward2 = 0
+        lastgaelam1 = lastgaelam2 = 0
+        for t in reversed(range(args.num_steps)):
+            # if learns[t]:
+            #     if dones[t+1]:
+            #         reward1 = rewards[t]
+            #         nextvalues1 = 0
+            #         lastgaelam1 = 0
+            #         done_used1 = True
+            #
+            #         reward2 = -rewards[t]
+            #         done_used2 = False
+            #     else:
+            #         if not done_used1:
+            #             reward1 = reward1
+            #             nextvalues1 = 0
+            #             lastgaelam1 = 0
+            #             done_used1 = True
+            #         else:
+            #             reward1 = rewards[t]
+            #         reward2 = reward2
+            #     delta1 = reward1 + args.gamma * nextvalues1 - values[t]
+            #     lastgaelam1_ = delta1 + args.gamma * args.gae_lambda * lastgaelam1
+            #     advantages[t] = lastgaelam1_
+            #     nextvalues1 = values[t]
+            #     lastgaelam1 = lastgaelam_
+            # else:
+            #     if dones[t+1]:
+            #         reward2 = rewards[t]
+            #         nextvalues2 = 0
+            #         lastgaelam2 = 0
+            #         done_used2 = True
+            #
+            #         reward1 = -rewards[t]
+            #         done_used1 = False
+            #     else:
+            #         if not done_used2:
+            #             reward2 = reward2
+            #             nextvalues2 = 0
+            #             lastgaelam2 = 0
+            #             done_used2 = True
+            #         else:
+            #             reward2 = rewards[t]
+            #         reward1 = reward1
+            #     delta2 = reward2 + args.gamma * nextvalues2 - values[t]
+            #     lastgaelam2_ = delta2 + args.gamma * args.gae_lambda * lastgaelam2
+            #     advantages[t] = lastgaelam2_
+            #     nextvalues2 = values[t]
+            #     lastgaelam2 = lastgaelam_
+
+            learn1 = learns[t]
+            learn2 = ~learn1
+            if t != args.num_steps - 1:
+                next_done = dones[t + 1]
+            sp = 2 * (learn1.int() - 0.5)
+            reward1 = torch.where(next_done, rewards[t] * sp, torch.where(learn1 & done_used1, 0, reward1))
+            reward2 = torch.where(next_done, rewards[t] * -sp, torch.where(learn2 & done_used2, 0, reward2))
+            real_done1 = next_done | ~done_used1
+            nextvalues1 = torch.where(real_done1, 0, nextvalues1)
+            lastgaelam1 = torch.where(real_done1, 0, lastgaelam1)
+            real_done2 = next_done | ~done_used2
+            nextvalues2 = torch.where(real_done2, 0, nextvalues2)
+            lastgaelam2 = torch.where(real_done2, 0, lastgaelam2)
+            done_used1 = torch.where(
+                next_done, learn1, torch.where(learn1 & ~done_used1, True, done_used1))
+            done_used2 = torch.where(
+                next_done, learn2, torch.where(learn2 & ~done_used2, True, done_used2))
+
+            delta1 = reward1 + args.gamma * nextvalues1 - values[t]
+            delta2 = reward2 + args.gamma * nextvalues2 - values[t]
+            lastgaelam1_ = delta1 + args.gamma * args.gae_lambda * lastgaelam1
+            lastgaelam2_ = delta2 + args.gamma * args.gae_lambda * lastgaelam2
+            advantages[t] = torch.where(learn1, lastgaelam1_, lastgaelam2_)
+            nextvalues1 = torch.where(learn1, values[t], nextvalues1)
+            nextvalues2 = torch.where(learn2, values[t], nextvalues2)
+            lastgaelam1 = torch.where(learn1, lastgaelam1_, lastgaelam1)
+            lastgaelam2 = torch.where(learn2, lastgaelam2_, lastgaelam2)
+        returns = advantages + values
+        bootstrap_time = time.time() - _start
+
+        _start = time.time()
+        agent.train()
+        # flatten the batch
+        b_obs = {
+            k: v.reshape((-1,) + v.shape[2:])
+            for k, v in obs.items()
+        }
+        b_logprobs = logprobs.reshape(-1)
+        b_actions = actions.reshape((-1,) + action_shape)
+        b_advantages = advantages.reshape(-1)
+        b_returns = returns.reshape(-1)
+        b_values = values.reshape(-1)
+        b_learns = learns.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]
+                mb_obs = {
+                    k: v[mb_inds] for k, v in b_obs.items()
+                }
+                old_approx_kl, approx_kl, clipfrac, pg_loss, v_loss, entropy_loss = \
+                    train_step(agent, scaler, mb_obs, b_actions[mb_inds], b_logprobs[mb_inds], b_advantages[mb_inds],
+                            b_returns[mb_inds], b_values[mb_inds], b_learns[mb_inds])
+                reduce_gradidents(optim_params, args.world_size)
+                nn.utils.clip_grad_norm_(optim_params, args.max_grad_norm)
+                scaler.step(optimizer)
+                scaler.update()
+                clipfracs.append(clipfrac.item())
+
+            if args.target_kl is not None and approx_kl > args.target_kl:
+                break
+        
+        train_time = time.time() - _start
+
+        if local_rank == 0:
+            fprint(f"train_time={train_time:.4f}, collect_time={collect_time:.4f}, bootstrap_time={bootstrap_time:.4f}")
+
+        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 rank == 0:
+            if iteration % args.save_interval == 0:
+                torch.save(agent.state_dict(), os.path.join(ckpt_dir, f"agent.pth"))
+
+            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)
+
+        SPS = int((global_step - warmup_steps) / (time.time() - start_time))
+
+        # Warmup at first few iterations for accurate SPS measurement
+        SPS_warmup_iters = 10
+        if iteration == SPS_warmup_iters:
+            start_time = time.time()
+            warmup_steps = global_step
+        if iteration > SPS_warmup_iters:
+            if local_rank == 0:
+                fprint(f"SPS: {SPS}")
+            if rank == 0:
+                writer.add_scalar("charts/SPS", SPS, global_step)
+
+        if iteration % args.eval_interval == 0:
+            # Eval with rule-based policy
+            _start = time.time()
+            episode_lengths = []
+            episode_rewards = []
+            eval_win_rates = []
+            e_obs = eval_envs.reset()[0]
+            while True:
+                e_obs = to_tensor(e_obs, dtype=torch.uint8)
+                e_logits = predict_step(traced_model, e_obs)[0]
+                e_probs = torch.softmax(e_logits, dim=-1)
+                e_probs = e_probs.cpu().numpy()
+                e_actions = e_probs.argmax(axis=1)
+
+                e_obs, e_rewards, e_dones, e_info = eval_envs.step(e_actions)
+
+                for idx, d in enumerate(e_dones):
+                    if d:
+                        episode_length = e_info['l'][idx]
+                        episode_reward = e_info['r'][idx]
+                        win = 1 if episode_reward > 0 else 0
+
+                        episode_lengths.append(episode_length)
+                        episode_rewards.append(episode_reward)
+                        eval_win_rates.append(win)
+                if len(episode_lengths) >= local_eval_episodes:
+                    break
+            
+            eval_return = np.mean(episode_rewards[:local_eval_episodes])
+            eval_ep_len = np.mean(episode_lengths[:local_eval_episodes])
+            eval_win_rate = np.mean(eval_win_rates[:local_eval_episodes])
+            eval_stats = torch.tensor([eval_return, eval_ep_len, eval_win_rate], dtype=torch.float32, device=device)
+
+            # sync the statistics
+            dist.all_reduce(eval_stats, op=dist.ReduceOp.AVG)
+            eval_return, eval_ep_len, eval_win_rate = eval_stats.cpu().numpy()
+            if rank == 0:
+                writer.add_scalar("charts/eval_return", eval_return, global_step)
+                writer.add_scalar("charts/eval_ep_len", eval_ep_len, global_step)
+                writer.add_scalar("charts/eval_win_rate", eval_win_rate, global_step)
+            if local_rank == 0:
+                eval_time = time.time() - _start
+                fprint(f"eval_time={eval_time:.4f}, eval_ep_return={eval_return:.4f}, eval_ep_len={eval_ep_len:.1f}, eval_win_rate={eval_win_rate:.4f}")
+
+            # Eval with old model
+
+    if args.world_size > 1:
+        dist.destroy_process_group()
+    envs.close()
+    if rank == 0:
+        torch.save(agent.state_dict(), os.path.join(ckpt_dir, f"agent_final.pth"))
+        writer.close()
+
+
+if __name__ == "__main__":
+    main()