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import argparse

import os

import random

import time

from collections import deque

from distutils.util import strtobool

import envpool

import gym

import numpy as np

import torch

import torch.nn as nn

import torch.optim as optim

from torch.distributions.categorical import Categorical

from torch.utils.tensorboard import SummaryWriter

from stable_baselines3.common.atari_wrappers import ( # isort:skip

ClipRewardEnv,

EpisodicLifeEnv,

FireResetEnv,

MaxAndSkipEnv,

NoopResetEnv,

)

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("--gym-id", type=str, default="BreakoutNoFrameskip-v4",

parser.add_argument("--gym-id", type=str, default="Pong-v5",

help="the id of the gym environment")

parser.add_argument("--learning-rate", type=float, default=2.5e-4,

help="the learning rate of the optimizer")

parser.add_argument("--seed", type=int, default=1,

help="seed of the experiment")

parser.add_argument("--total-timesteps", type=int, default=10000000,

help="total timesteps of the experiments")

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="ppo-implementation-details",

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="weather to capture videos of the agent performances (check out `videos` folder)")

# Algorithm specific arguments

parser.add_argument("--num-envs", type=int, default=8,

help="the number of parallel game environments")

parser.add_argument("--num-steps", type=int, default=128,

help="the number of steps to run in each environment per policy rollout")

parser.add_argument("--anneal-lr", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,

help="Toggle learning rate annealing for policy and value networks")

parser.add_argument("--gae", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,

help="Use GAE for advantage computation")

parser.add_argument("--gamma", type=float, default=0.99,

help="the discount factor gamma")

parser.add_argument("--gae-lambda", type=float, default=0.95,

help="the lambda for the general advantage estimation")

parser.add_argument("--num-minibatches", type=int, default=4,

help="the number of mini-batches")

parser.add_argument("--update-epochs", type=int, default=4,

help="the K epochs to update the policy")

parser.add_argument("--norm-adv", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,

help="Toggles advantages normalization")

parser.add_argument("--clip-coef", type=float, default=0.1,

help="the surrogate clipping coefficient")

parser.add_argument("--clip-vloss", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,

help="Toggles whether or not to use a clipped loss for the value function, as per the paper.")

parser.add_argument("--ent-coef", type=float, default=0.01,

help="coefficient of the entropy")

parser.add_argument("--vf-coef", type=float, default=0.5,

help="coefficient of the value function")

parser.add_argument("--max-grad-norm", type=float, default=0.5,

help="the maximum norm for the gradient clipping")

parser.add_argument("--target-kl", type=float, default=None,

help="the target KL divergence threshold")

args = parser.parse_args()

args.batch_size = int(args.num_envs * args.num_steps)

args.minibatch_size = int(args.batch_size // args.num_minibatches)

# fmt: on

return args

def make_env(gym_id, seed, idx, capture_video, run_name):

class RecordEpisodeStatistics(gym.Wrapper):

def thunk():

def __init__(self, env, deque_size=100):

env = gym.make(gym_id)

super(RecordEpisodeStatistics, self).__init__(env)

env = gym.wrappers.RecordEpisodeStatistics(env)

self.num_envs = getattr(env, "num_envs", 1)

if capture_video:

self.episode_returns = None

if idx == 0:

self.episode_lengths = None

env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")

# get if the env has lives

env = NoopResetEnv(env, noop_max=30)

self.has_lives = False

env = MaxAndSkipEnv(env, skip=4)

env.reset()

env = EpisodicLifeEnv(env)

info = env.step(np.zeros(self.num_envs, dtype=int))[-1]

if "FIRE" in env.unwrapped.get_action_meanings():

if info["lives"].sum() > 0:

env = FireResetEnv(env)

self.has_lives = True

env = ClipRewardEnv(env)

print("env has lives")

env = gym.wrappers.ResizeObservation(env, (84, 84))

env = gym.wrappers.GrayScaleObservation(env)

env = gym.wrappers.FrameStack(env, 4)

env.seed(seed)

env.action_space.seed(seed)

env.observation_space.seed(seed)

return env

return thunk

def reset(self, **kwargs):

observations = super(RecordEpisodeStatistics, self).reset(**kwargs)

self.episode_returns = np.zeros(self.num_envs, dtype=np.float32)

self.episode_lengths = np.zeros(self.num_envs, dtype=np.int32)

self.lives = np.zeros(self.num_envs, dtype=np.int32)

self.returned_episode_returns = np.zeros(self.num_envs, dtype=np.float32)

self.returned_episode_lengths = np.zeros(self.num_envs, dtype=np.int32)

return observations

def step(self, action):

observations, rewards, dones, infos = super(RecordEpisodeStatistics, self).step(action)

self.episode_returns += infos["reward"]

self.episode_lengths += 1

self.returned_episode_returns[:] = self.episode_returns

self.returned_episode_lengths[:] = self.episode_lengths

all_lives_exhausted = infos["lives"] == 0

if self.has_lives:

self.episode_returns *= 1 - all_lives_exhausted

self.episode_lengths *= 1 - all_lives_exhausted

else:

self.episode_returns *= 1 - dones

self.episode_lengths *= 1 - dones

infos["r"] = self.returned_episode_returns

infos["l"] = self.returned_episode_lengths

return (

observations,

rewards,

dones,

infos,

)

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(Agent, self).__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)),

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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.optim as optim
from torch.distributions.categorical import Categorical
from torch.utils.tensorboard import SummaryWriter

from stable_baselines3.common.atari_wrappers import (  # isort:skip
    ClipRewardEnv,
    EpisodicLifeEnv,
    FireResetEnv,
    MaxAndSkipEnv,
    NoopResetEnv,
)

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("--gym-id", type=str, default="BreakoutNoFrameskip-v4",
        help="the id of the gym environment")
    parser.add_argument("--learning-rate", type=float, default=2.5e-4,
        help="the learning rate of the optimizer")
    parser.add_argument("--seed", type=int, default=1,
        help="seed of the experiment")
    parser.add_argument("--total-timesteps", type=int, default=10000000,
        help="total timesteps of the experiments")
    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="ppo-implementation-details",
        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="weather to capture videos of the agent performances (check out `videos` folder)")

# Algorithm specific arguments
    parser.add_argument("--num-envs", type=int, default=8,
        help="the number of parallel game environments")
    parser.add_argument("--num-steps", type=int, default=128,
        help="the number of steps to run in each environment per policy rollout")
    parser.add_argument("--anneal-lr", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
        help="Toggle learning rate annealing for policy and value networks")
    parser.add_argument("--gae", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
        help="Use GAE for advantage computation")
    parser.add_argument("--gamma", type=float, default=0.99,
        help="the discount factor gamma")
    parser.add_argument("--gae-lambda", type=float, default=0.95,
        help="the lambda for the general advantage estimation")
    parser.add_argument("--num-minibatches", type=int, default=4,
        help="the number of mini-batches")
    parser.add_argument("--update-epochs", type=int, default=4,
        help="the K epochs to update the policy")
    parser.add_argument("--norm-adv", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
        help="Toggles advantages normalization")
    parser.add_argument("--clip-coef", type=float, default=0.1,
        help="the surrogate clipping coefficient")
    parser.add_argument("--clip-vloss", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
        help="Toggles whether or not to use a clipped loss for the value function, as per the paper.")
    parser.add_argument("--ent-coef", type=float, default=0.01,
        help="coefficient of the entropy")
    parser.add_argument("--vf-coef", type=float, default=0.5,
        help="coefficient of the value function")
    parser.add_argument("--max-grad-norm", type=float, default=0.5,
        help="the maximum norm for the gradient clipping")
    parser.add_argument("--target-kl", type=float, default=None,
        help="the target KL divergence threshold")
    args = parser.parse_args()
    args.batch_size = int(args.num_envs * args.num_steps)
    args.minibatch_size = int(args.batch_size // args.num_minibatches)
    # fmt: on
    return args

def make_env(gym_id, seed, idx, capture_video, run_name):
    def thunk():
        env = gym.make(gym_id)
        env = gym.wrappers.RecordEpisodeStatistics(env)
        if capture_video:
            if idx == 0:
                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
        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.seed(seed)
        env.action_space.seed(seed)
        env.observation_space.seed(seed)
        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(Agent, self).__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__":
    args = parse_args()
    run_name = f"{args.gym_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.gym_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"

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

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

# TRY NOT TO MODIFY: start the game
    global_step = 0
    start_time = time.time()
    next_obs = torch.Tensor(envs.reset()).to(device)
    next_done = torch.zeros(args.num_envs).to(device)
    num_updates = args.total_timesteps // args.batch_size

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

for step in range(0, args.num_steps):
            global_step += 1 * 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, done, info = envs.step(action.cpu().numpy())
            rewards[step] = torch.tensor(reward).to(device).view(-1)
            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)

for item in info:
                if "episode" in item.keys():
                    print(f"global_step={global_step}, episodic_return={item['episode']['r']}")
                    writer.add_scalar("charts/episodic_return", item["episode"]["r"], global_step)
                    writer.add_scalar("charts/episodic_length", item["episode"]["l"], global_step)
                    break

# bootstrap value if not done
        with torch.no_grad():
            next_value = agent.get_value(next_obs).reshape(1, -1)
            if args.gae:
                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
            else:
                returns = torch.zeros_like(rewards).to(device)
                for t in reversed(range(args.num_steps)):
                    if t == args.num_steps - 1:
                        nextnonterminal = 1.0 - next_done
                        next_return = next_value
                    else:
                        nextnonterminal = 1.0 - dones[t + 1]
                        next_return = returns[t + 1]
                    returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
                advantages = returns - 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.batch_size)
        clipfracs = []
        for epoch in range(args.update_epochs):
            np.random.shuffle(b_inds)
            for start in range(0, args.batch_size, args.minibatch_size):
                end = start + args.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()
                nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
                optimizer.step()

if args.target_kl is not None:
                if 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
        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()
    writer.close()

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import argparse
import os
import random
import time
from collections import deque
from distutils.util import strtobool

import envpool
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.distributions.categorical import Categorical
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("--gym-id", type=str, default="Pong-v5",
        help="the id of the gym environment")
    parser.add_argument("--learning-rate", type=float, default=2.5e-4,
        help="the learning rate of the optimizer")
    parser.add_argument("--seed", type=int, default=1,
        help="seed of the experiment")
    parser.add_argument("--total-timesteps", type=int, default=10000000,
        help="total timesteps of the experiments")
    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="ppo-implementation-details",
        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="weather to capture videos of the agent performances (check out `videos` folder)")

class RecordEpisodeStatistics(gym.Wrapper):
    def __init__(self, env, deque_size=100):
        super(RecordEpisodeStatistics, self).__init__(env)
        self.num_envs = getattr(env, "num_envs", 1)
        self.episode_returns = None
        self.episode_lengths = None
        # get if the env has lives
        self.has_lives = False
        env.reset()
        info = env.step(np.zeros(self.num_envs, dtype=int))[-1]
        if info["lives"].sum() > 0:
            self.has_lives = True
            print("env has lives")

def reset(self, **kwargs):
        observations = super(RecordEpisodeStatistics, self).reset(**kwargs)
        self.episode_returns = np.zeros(self.num_envs, dtype=np.float32)
        self.episode_lengths = np.zeros(self.num_envs, dtype=np.int32)
        self.lives = np.zeros(self.num_envs, dtype=np.int32)
        self.returned_episode_returns = np.zeros(self.num_envs, dtype=np.float32)
        self.returned_episode_lengths = np.zeros(self.num_envs, dtype=np.int32)
        return observations

def step(self, action):
        observations, rewards, dones, infos = super(RecordEpisodeStatistics, self).step(action)
        self.episode_returns += infos["reward"]
        self.episode_lengths += 1
        self.returned_episode_returns[:] = self.episode_returns
        self.returned_episode_lengths[:] = self.episode_lengths
        all_lives_exhausted = infos["lives"] == 0
        if self.has_lives:
            self.episode_returns *= 1 - all_lives_exhausted
            self.episode_lengths *= 1 - all_lives_exhausted
        else:
            self.episode_returns *= 1 - dones
            self.episode_lengths *= 1 - dones
        infos["r"] = self.returned_episode_returns
        infos["l"] = self.returned_episode_lengths
        return (
            observations,
            rewards,
            dones,
            infos,
        )

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

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

if __name__ == "__main__":
    args = parse_args()
    run_name = f"{args.gym_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
    if args.track:
        import wandb

# 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 = envpool.make(
        args.gym_id,
        env_type="gym",
        num_envs=args.num_envs,
        episodic_life=True,
        reward_clip=True,
    )
    envs.num_envs = args.num_envs
    envs.single_action_space = envs.action_space
    envs.single_observation_space = envs.observation_space
    envs = RecordEpisodeStatistics(envs)
    assert isinstance(envs.action_space, gym.spaces.Discrete), "only discrete action space is supported"

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

for step in range(0, args.num_steps):
            global_step += 1 * args.num_envs
            obs[step] = next_obs
            dones[step] = next_done

for idx, d in enumerate(done):
                if d and info["lives"][idx] == 0:
                    print(f"global_step={global_step}, episodic_return={info['r'][idx]}")
                    avg_returns.append(info["r"][idx])
                    writer.add_scalar("charts/avg_episodic_return", np.average(avg_returns), global_step)
                    writer.add_scalar("charts/episodic_return", info["r"][idx], global_step)
                    writer.add_scalar("charts/episodic_length", info["l"][idx], global_step)

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

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

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

if args.target_kl is not None:
                if 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

envs.close()
    writer.close()

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PPO + Envpool