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Brax Diff

Created 3 years agoDiff never expires

13 removals

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18 additions

334 lines

import argparse

import os

import random

import time

from distutils.util import strtobool

import gym

import pybullet_envs # fmt: off

import numpy as np

import torch

import torch.nn as nn

import torch.optim as optim

from torch.distributions.normal import Normal

from torch.utils.tensorboard import SummaryWriter

from brax.envs import create_gym_env # fmt: off

from brax.envs.to_torch import JaxToTorchWrapper # fmt: off

def parse_args():

# fmt: off

parser = argparse.ArgumentParser()

parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),

parser.add_argument('--exp-name', type=str, default="ppo_brax",

help='the name of this experiment')

parser.add_argument('--gym-id', type=str, default="HalfCheetahBulletEnv-v0",

parser.add_argument('--gym-id', type=str, default="halfcheetah",

help='the id of the gym environment')

parser.add_argument('--learning-rate', type=float, default=3e-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=2000000,

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="cleanRL",

help="the wandb's project name")

parser.add_argument('--wandb-entity', type=str, default=None,

help="the entity (team) of wandb's project")

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

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

# Algorithm specific arguments

parser.add_argument('--num-envs', type=int, default=1,

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

help='the number of parallel game environments')

parser.add_argument('--num-steps', type=int, default=2048,

parser.add_argument('--num-steps', type=int, default=10,

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=32,

help='the number of mini-batches')

parser.add_argument('--update-epochs', type=int, default=10,

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=0.2,

help="the surrogate clipping coefficient")

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.')

parser.add_argument('--ent-coef', type=float, default=0.0,

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')

parser.add_argument('-f', "--dummy-colab",

help='the dummpy option to make it work with colab')

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 = gym.wrappers.ClipAction(env)

env = gym.wrappers.NormalizeObservation(env)

env = gym.wrappers.TransformObservation(env, lambda obs: np.clip(obs, -10, 10))

env = gym.wrappers.NormalizeReward(env)

env = gym.wrappers.TransformReward(env, lambda reward: np.clip(reward, -10, 10))

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.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=1.0),

)

self.actor_mean = 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, np.prod(envs.single_action_space.shape)), std=0.01),

)

self.actor_logstd = nn.Parameter(torch.zeros(1, np.prod(envs.single_action_space.shape)))

def get_value(self, x):

return self.critic(x)

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

action_mean = self.actor_mean(x)

action_logstd = self.actor_logstd.expand_as(action_mean)

action_std = torch.exp(action_logstd)

probs = Normal(action_mean, action_std)

if action is None:

action = probs.sample()

return action, probs.log_prob(action).sum(1), probs.entropy().sum(1), 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.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(

dummy_value = torch.ones(1, device=device)

[make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name) for i in range(args.num_envs)]

envs = create_gym_env(args.gym_id, batch_size=args.num_envs)

)

envs.is_vector_env = True

envs = JaxToTorchWrapper(envs, device=device)

assert isinstance(envs.single_action_space, gym.spaces.Box), "only continuous 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_obs = envs.reset()

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())

next_obs, reward, done, info = envs.step(action)

rewards[step] = torch.tensor(reward).to(device).view(-1)

rewards[step] = reward.view(-1)

next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)

next_obs, next_done = next_obs, done

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)

# Optimizaing 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[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/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 distutils.util import strtobool

import gym
import pybullet_envs # fmt: off
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.distributions.normal import Normal
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="HalfCheetahBulletEnv-v0",
        help='the id of the gym environment')
    parser.add_argument('--learning-rate', type=float, default=3e-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=2000000,
        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="cleanRL",
        help="the wandb's project name")
    parser.add_argument('--wandb-entity', type=str, default=None,
        help="the entity (team) of wandb's project")
    parser.add_argument('--capture-video', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
        help='weather to capture videos of the agent performances (check out `videos` folder)')

# Algorithm specific arguments
    parser.add_argument('--num-envs', type=int, default=1,
        help='the number of parallel game environments')
    parser.add_argument('--num-steps', type=int, default=2048,
        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=32,
        help='the number of mini-batches')
    parser.add_argument('--update-epochs', type=int, default=10,
        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=0.2,
        help="the surrogate clipping coefficient")
    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.')
    parser.add_argument('--ent-coef', type=float, default=0.0,
        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 = gym.wrappers.ClipAction(env)
        env = gym.wrappers.NormalizeObservation(env)
        env = gym.wrappers.TransformObservation(env, lambda obs: np.clip(obs, -10, 10))
        env = gym.wrappers.NormalizeReward(env)
        env = gym.wrappers.TransformReward(env, lambda reward: np.clip(reward, -10, 10))
        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.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=1.0),
        )
        self.actor_mean = 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, np.prod(envs.single_action_space.shape)), std=0.01),
        )
        self.actor_logstd = nn.Parameter(torch.zeros(1, np.prod(envs.single_action_space.shape)))
    
    def get_value(self, x):
        return self.critic(x)

def get_action_and_value(self, x, action=None):
        action_mean = self.actor_mean(x)
        action_logstd = self.actor_logstd.expand_as(action_mean)
        action_std = torch.exp(action_logstd)
        probs = Normal(action_mean, action_std)
        if action is None:
            action = probs.sample()
        return action, probs.log_prob(action).sum(1), probs.entropy().sum(1), 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.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.Box), "only continuous 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)

# Optimizaing 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[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/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()

Geänderter text

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 torch.distributions.normal import Normal
from torch.utils.tensorboard import SummaryWriter

from brax.envs import create_gym_env  # fmt: off
from brax.envs.to_torch import JaxToTorchWrapper  # fmt: off

def parse_args():
    # fmt: off
    parser = argparse.ArgumentParser()
    parser.add_argument('--exp-name', type=str, default="ppo_brax",
        help='the name of this experiment')
    parser.add_argument('--gym-id', type=str, default="halfcheetah",
        help='the id of the gym environment')
    parser.add_argument('--learning-rate', type=float, default=3e-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=2000000,
        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="cleanRL",
        help="the wandb's project name")
    parser.add_argument('--wandb-entity', type=str, default=None,
        help="the entity (team) of wandb's project")
    parser.add_argument('--capture-video', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
        help='weather to capture videos of the agent performances (check out `videos` folder)')

# Algorithm specific arguments
    parser.add_argument('--num-envs', type=int, default=128,
        help='the number of parallel game environments')
    parser.add_argument('--num-steps', type=int, default=10,
        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=32,
        help='the number of mini-batches')
    parser.add_argument('--update-epochs', type=int, default=10,
        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=0.2,
        help="the surrogate clipping coefficient")
    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.')
    parser.add_argument('--ent-coef', type=float, default=0.0,
        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')
    parser.add_argument('-f', "--dummy-colab",
        help='the dummpy option to make it work with colab')
    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

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

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
    dummy_value = torch.ones(1, device=device)
    envs = create_gym_env(args.gym_id, batch_size=args.num_envs)
    envs.is_vector_env = True
    envs = JaxToTorchWrapper(envs, device=device)
    assert isinstance(envs.single_action_space, gym.spaces.Box), "only continuous action space is supported"
 
    agent = Agent(envs).to(device)
    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)

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

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

# TRY NOT TO MODIFY: execute the game and log data.
            next_obs, reward, done, info = envs.step(action)
            rewards[step] = reward.view(-1)
            next_obs, next_done = next_obs, done

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

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()