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Gym's Vector API vs SB3's Vector API

Created 3 years agoDiff never expires

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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 gym.spaces import Discrete

from gym.wrappers import Monitor

from stable_baselines3.common.vec_env import DummyVecEnv, VecFrameStack

from gym.vector import SyncVectorEnv

from torch.distributions.categorical import Categorical

from torch.utils.tensorboard import SummaryWriter

def parse_args():

# fmt: off

parser = argparse.ArgumentParser(description='PPO agent')

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="CartPole-v1",

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=25000,

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 not be enabled by default')

parser.add_argument('--prod-mode', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,

help='run the script in production mode and use wandb to log outputs')

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)')

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")

# Algorithm specific arguments

parser.add_argument('--n-minibatch', type=int, default=4,

help='the number of mini batch')

parser.add_argument('--num-envs', type=int, default=4,

help='the number of parallel game environment')

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

help='the number of steps per game environment')

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('--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('--clip-coef', type=float, default=0.2,

help="the surrogate clipping coefficient")

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

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

parser.add_argument('--kle-stop', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,

help='If toggled, the policy updates will be early stopped w.r.t target-kl')

parser.add_argument('--target-kl', type=float, default=0.03,

help='the target-kl variable that is referred by --kl')

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

help='Use GAE for advantage computation')

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

help="Toggles advantages normalization")

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('--clip-vloss', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,

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

args = parser.parse_args()

if not args.seed:

args.seed = int(time.time())

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

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

# 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 = Monitor(env, f'videos/{run_name}')

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, frames=4):

super(Agent, self).__init__()

self.critic = nn.Sequential(

layer_init(nn.Linear(np.array(envs.observation_space.shape).prod(), 64)),

layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)),

nn.Tanh(),

layer_init(nn.Linear(64, 64)),

nn.Tanh(),

layer_init(nn.Linear(64, 1), std=1.),

)

self.actor = nn.Sequential(

layer_init(nn.Linear(np.array(envs.observation_space.shape).prod(), 64)),

layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)),

nn.Tanh(),

layer_init(nn.Linear(64, 64)),

nn.Tanh(),

layer_init(nn.Linear(64, envs.action_space.n), std=0.01),

layer_init(nn.Linear(64, envs.single_action_space.n), std=0.01),

)

def get_action_and_value(self, x, action=None):

logits = self.actor(x)

probs = Categorical(logits=logits)

if action is None:

action = probs.sample()

return action, probs.log_prob(action), probs.entropy(), self.critic(x)

def get_value(self, x):

return self.critic(x)

if __name__ == "__main__":

args = parse_args()

run_name = f"{args.gym_id}__{args.exp_name}__{args.seed}__{int(time.time())}"

if args.prod_mode:

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()])),

)

device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")

# 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

# env setup

envs = DummyVecEnv(

envs = 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.action_space, Discrete), "only discrete action space is supported"

assert isinstance(envs.single_action_space, Discrete), "only discrete action space is supported"

agent = Agent(envs).to(device)

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

if args.anneal_lr:

# https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/ppo2/defaults.py#L20

lr = lambda f: f * args.learning_rate

# ALGO Logic: Storage for epoch data

obs = torch.zeros((args.num_steps, args.num_envs) + envs.observation_space.shape).to(device)

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.action_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()

# Note how `next_obs` and `next_done` are used; their usage is equivalent to

# https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/blob/84a7582477fb0d5c82ad6d850fe476829dddd2e1/a2c_ppo_acktr/storage.py#L60

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 = lr(frac)

optimizer.param_groups[0]["lr"] = lrnow

# TRY NOT TO MODIFY: prepare the execution of the game.

for step in range(0, args.num_steps):

global_step += 1 * args.num_envs

obs[step] = next_obs

dones[step] = next_done

# ALGO LOGIC: put action logic here

with torch.no_grad():

action, logproba, _, vs = agent.get_action_and_value(next_obs)

values[step] = vs.flatten()

actions[step] = action

logprobs[step] = logproba

# TRY NOT TO MODIFY: execute the game and log data.

next_obs, rs, ds, infos = envs.step(action.cpu().numpy())

next_obs = torch.Tensor(next_obs).to(device)

rewards[step], next_done = torch.tensor(rs).to(device).view(-1), torch.Tensor(ds).to(device)

for info in infos:

if "episode" in info.keys():

print(f"global_step={global_step}, episode_reward={info['episode']['r']}")

writer.add_scalar("charts/episode_reward", info["episode"]["r"], global_step)

break

# bootstrap reward if not done. reached the batch limit

with torch.no_grad():

last_value = agent.get_value(next_obs.to(device)).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 = last_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 = last_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.observation_space.shape)

b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)

b_logprobs = logprobs.reshape(-1)

b_actions = actions.reshape((-1,) + envs.action_space.shape)

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)

# Optimizaing the policy and value network

inds = np.arange(

args.batch_size,

)

for i_epoch_pi in range(args.update_epochs):

np.random.shuffle(inds)

for start in range(0, args.batch_size, args.minibatch_size):

end = start + args.minibatch_size

minibatch_ind = inds[start:end]

mb_advantages = b_advantages[minibatch_ind]

if args.norm_adv:

mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)

_, newlogproba, entropy, new_values = agent.get_action_and_value(

b_obs[minibatch_ind], b_actions.long()[minibatch_ind].to(device)

)

ratio = (newlogproba - b_logprobs[minibatch_ind]).exp()

# calculate approx_kl http://joschu.net/blog/kl-approx.html

with torch.no_grad():

log_ratio = newlogproba - b_logprobs[minibatch_ind]

approx_kl = ((log_ratio.exp() - 1) - log_ratio).mean()

# 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()

entropy_loss = entropy.mean()

# Value loss

new_values = new_values.view(-1)

if args.clip_vloss:

v_loss_unclipped = (new_values - b_returns[minibatch_ind]) ** 2

v_clipped = b_values[minibatch_ind] + torch.clamp(

new_values - b_values[minibatch_ind],

-args.clip_coef,

args.clip_coef,

)

v_loss_clipped = (v_clipped - b_returns[minibatch_ind]) ** 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 * ((new_values - b_returns[minibatch_ind]) ** 2).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.kle_stop:

if approx_kl > args.target_kl:

break

# 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.mean().item(), global_step)

writer.add_scalar("losses/approx_kl", approx_kl.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)

envs.close()

writer.close()

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Open file

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 gym.spaces import Discrete
from gym.wrappers import Monitor
from stable_baselines3.common.vec_env import DummyVecEnv, VecFrameStack
from torch.distributions.categorical import Categorical
from torch.utils.tensorboard import SummaryWriter


def parse_args():
    # fmt: off
    parser = argparse.ArgumentParser(description='PPO agent')
    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="CartPole-v1",
        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=25000,
        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 not be enabled by default')
    parser.add_argument('--prod-mode', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
        help='run the script in production mode and use wandb to log outputs')
    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)')
    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")

    # Algorithm specific arguments
    parser.add_argument('--n-minibatch', type=int, default=4,
        help='the number of mini batch')
    parser.add_argument('--num-envs', type=int, default=4,
        help='the number of parallel game environment')
    parser.add_argument('--num-steps', type=int, default=128,
        help='the number of steps per game environment')
    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('--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('--clip-coef', type=float, default=0.2,
        help="the surrogate clipping coefficient")
    parser.add_argument('--update-epochs', type=int, default=4,
        help="the K epochs to update the policy")
    parser.add_argument('--kle-stop', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
        help='If toggled, the policy updates will be early stopped w.r.t target-kl')
    parser.add_argument('--target-kl', type=float, default=0.03,
        help='the target-kl variable that is referred by --kl')
    parser.add_argument('--gae', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
        help='Use GAE for advantage computation')
    parser.add_argument('--norm-adv', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
        help="Toggles advantages normalization")
    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('--clip-vloss', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
        help='Toggles wheter or not to use a clipped loss for the value function, as per the paper.')
    args = parser.parse_args()
    if not args.seed:
        args.seed = int(time.time())
    args.batch_size = int(args.num_envs * args.num_steps)
    args.minibatch_size = int(args.batch_size // args.n_minibatch)
    # 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 = Monitor(env, f'videos/{run_name}')
        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, frames=4):
        super(Agent, self).__init__()
        self.critic = nn.Sequential(
            layer_init(nn.Linear(np.array(envs.observation_space.shape).prod(), 64)),
            nn.Tanh(),
            layer_init(nn.Linear(64, 64)),
            nn.Tanh(),
            layer_init(nn.Linear(64, 1), std=1.),
        )
        self.actor = nn.Sequential(
            layer_init(nn.Linear(np.array(envs.observation_space.shape).prod(), 64)),
            nn.Tanh(),
            layer_init(nn.Linear(64, 64)),
            nn.Tanh(),
            layer_init(nn.Linear(64,  envs.action_space.n), std=0.01),
        )

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

    def get_value(self, x):
        return self.critic(x)


if __name__ == "__main__":
    args = parse_args()
    run_name = f"{args.gym_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
    if args.prod_mode:
        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()])),
    )
    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")

    # 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

    # env setup
    envs = DummyVecEnv(
        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name) 
    for i in range(args.num_envs)])
    assert isinstance(envs.action_space, Discrete), "only discrete action space is supported"

    agent = Agent(envs).to(device)
    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
    if args.anneal_lr:
        # https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/ppo2/defaults.py#L20
        lr = lambda f: f * args.learning_rate

    # ALGO Logic: Storage for epoch data
    obs = torch.zeros((args.num_steps, args.num_envs) + envs.observation_space.shape).to(device)
    actions = torch.zeros((args.num_steps, args.num_envs) + envs.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()
    # Note how `next_obs` and `next_done` are used; their usage is equivalent to
    # https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/blob/84a7582477fb0d5c82ad6d850fe476829dddd2e1/a2c_ppo_acktr/storage.py#L60
    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 = lr(frac)
            optimizer.param_groups[0]["lr"] = lrnow

        # TRY NOT TO MODIFY: prepare the execution of the game.
        for step in range(0, args.num_steps):
            global_step += 1 * args.num_envs
            obs[step] = next_obs
            dones[step] = next_done

            # ALGO LOGIC: put action logic here
            with torch.no_grad():
                action, logproba, _, vs = agent.get_action_and_value(next_obs)
                values[step] = vs.flatten()

            actions[step] = action
            logprobs[step] = logproba

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

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

        # bootstrap reward if not done. reached the batch limit
        with torch.no_grad():
            last_value = agent.get_value(next_obs.to(device)).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 = last_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 = last_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.observation_space.shape)
        b_logprobs = logprobs.reshape(-1)
        b_actions = actions.reshape((-1,) + envs.action_space.shape)
        b_advantages = advantages.reshape(-1)
        b_returns = returns.reshape(-1)
        b_values = values.reshape(-1)

        # Optimizaing the policy and value network
        inds = np.arange(
            args.batch_size,
        )
        for i_epoch_pi in range(args.update_epochs):
            np.random.shuffle(inds)
            for start in range(0, args.batch_size, args.minibatch_size):
                end = start + args.minibatch_size
                minibatch_ind = inds[start:end]
                mb_advantages = b_advantages[minibatch_ind]
                if args.norm_adv:
                    mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)

                _, newlogproba, entropy, new_values = agent.get_action_and_value(
                    b_obs[minibatch_ind], b_actions.long()[minibatch_ind].to(device)
                )
                ratio = (newlogproba - b_logprobs[minibatch_ind]).exp()

                # calculate approx_kl http://joschu.net/blog/kl-approx.html
                with torch.no_grad():
                    log_ratio = newlogproba - b_logprobs[minibatch_ind]
                    approx_kl = ((log_ratio.exp() - 1) - log_ratio).mean()

                # 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()
                entropy_loss = entropy.mean()

                # Value loss
                new_values = new_values.view(-1)
                if args.clip_vloss:
                    v_loss_unclipped = (new_values - b_returns[minibatch_ind]) ** 2
                    v_clipped = b_values[minibatch_ind] + torch.clamp(
                        new_values - b_values[minibatch_ind],
                        -args.clip_coef,
                        args.clip_coef,
                    )
                    v_loss_clipped = (v_clipped - b_returns[minibatch_ind]) ** 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 * ((new_values - b_returns[minibatch_ind]) ** 2).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.kle_stop:
                if approx_kl > args.target_kl:
                    break

        # 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.mean().item(), global_step)
        writer.add_scalar("losses/approx_kl", approx_kl.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)

    envs.close()
    writer.close()

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Open file

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 gym.spaces import Discrete
from gym.wrappers import Monitor
from gym.vector import SyncVectorEnv
from torch.distributions.categorical import Categorical
from torch.utils.tensorboard import SummaryWriter


def parse_args():
    # fmt: off
    parser = argparse.ArgumentParser(description='PPO agent')
    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="CartPole-v1",
        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=25000,
        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 not be enabled by default')
    parser.add_argument('--prod-mode', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
        help='run the script in production mode and use wandb to log outputs')
    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)')
    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")

    # Algorithm specific arguments
    parser.add_argument('--n-minibatch', type=int, default=4,
        help='the number of mini batch')
    parser.add_argument('--num-envs', type=int, default=4,
        help='the number of parallel game environment')
    parser.add_argument('--num-steps', type=int, default=128,
        help='the number of steps per game environment')
    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('--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('--clip-coef', type=float, default=0.2,
        help="the surrogate clipping coefficient")
    parser.add_argument('--update-epochs', type=int, default=4,
        help="the K epochs to update the policy")
    parser.add_argument('--kle-stop', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
        help='If toggled, the policy updates will be early stopped w.r.t target-kl')
    parser.add_argument('--target-kl', type=float, default=0.03,
        help='the target-kl variable that is referred by --kl')
    parser.add_argument('--gae', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
        help='Use GAE for advantage computation')
    parser.add_argument('--norm-adv', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
        help="Toggles advantages normalization")
    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('--clip-vloss', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
        help='Toggles wheter or not to use a clipped loss for the value function, as per the paper.')
    args = parser.parse_args()
    if not args.seed:
        args.seed = int(time.time())
    args.batch_size = int(args.num_envs * args.num_steps)
    args.minibatch_size = int(args.batch_size // args.n_minibatch)
    # 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 = Monitor(env, f'videos/{run_name}')
        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, frames=4):
        super(Agent, self).__init__()
        self.critic = nn.Sequential(
            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)),
            nn.Tanh(),
            layer_init(nn.Linear(64, 64)),
            nn.Tanh(),
            layer_init(nn.Linear(64, 1), std=1.),
        )
        self.actor = nn.Sequential(
            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)),
            nn.Tanh(),
            layer_init(nn.Linear(64, 64)),
            nn.Tanh(),
            layer_init(nn.Linear(64,  envs.single_action_space.n), std=0.01),
        )

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

    def get_value(self, x):
        return self.critic(x)


if __name__ == "__main__":
    args = parse_args()
    run_name = f"{args.gym_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
    if args.prod_mode:
        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()])),
    )
    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")

    # 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

    # env setup
    envs = 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, Discrete), "only discrete action space is supported"

    agent = Agent(envs).to(device)
    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
    if args.anneal_lr:
        # https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/ppo2/defaults.py#L20
        lr = lambda f: f * args.learning_rate

    # ALGO Logic: Storage for epoch data
    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()
    # Note how `next_obs` and `next_done` are used; their usage is equivalent to
    # https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/blob/84a7582477fb0d5c82ad6d850fe476829dddd2e1/a2c_ppo_acktr/storage.py#L60
    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 = lr(frac)
            optimizer.param_groups[0]["lr"] = lrnow

        # TRY NOT TO MODIFY: prepare the execution of the game.
        for step in range(0, args.num_steps):
            global_step += 1 * args.num_envs
            obs[step] = next_obs
            dones[step] = next_done

            # ALGO LOGIC: put action logic here
            with torch.no_grad():
                action, logproba, _, vs = agent.get_action_and_value(next_obs)
                values[step] = vs.flatten()

            actions[step] = action
            logprobs[step] = logproba

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

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

        # bootstrap reward if not done. reached the batch limit
        with torch.no_grad():
            last_value = agent.get_value(next_obs.to(device)).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 = last_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 = last_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)

        # Optimizaing the policy and value network
        inds = np.arange(
            args.batch_size,
        )
        for i_epoch_pi in range(args.update_epochs):
            np.random.shuffle(inds)
            for start in range(0, args.batch_size, args.minibatch_size):
                end = start + args.minibatch_size
                minibatch_ind = inds[start:end]
                mb_advantages = b_advantages[minibatch_ind]
                if args.norm_adv:
                    mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)

                _, newlogproba, entropy, new_values = agent.get_action_and_value(
                    b_obs[minibatch_ind], b_actions.long()[minibatch_ind].to(device)
                )
                ratio = (newlogproba - b_logprobs[minibatch_ind]).exp()

                # calculate approx_kl http://joschu.net/blog/kl-approx.html
                with torch.no_grad():
                    log_ratio = newlogproba - b_logprobs[minibatch_ind]
                    approx_kl = ((log_ratio.exp() - 1) - log_ratio).mean()

                # 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()
                entropy_loss = entropy.mean()

                # Value loss
                new_values = new_values.view(-1)
                if args.clip_vloss:
                    v_loss_unclipped = (new_values - b_returns[minibatch_ind]) ** 2
                    v_clipped = b_values[minibatch_ind] + torch.clamp(
                        new_values - b_values[minibatch_ind],
                        -args.clip_coef,
                        args.clip_coef,
                    )
                    v_loss_clipped = (v_clipped - b_returns[minibatch_ind]) ** 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 * ((new_values - b_returns[minibatch_ind]) ** 2).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.kle_stop:
                if approx_kl > args.target_kl:
                    break

        # 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.mean().item(), global_step)
        writer.add_scalar("losses/approx_kl", approx_kl.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)

    envs.close()
    writer.close()