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Implementation of invalid action masking

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import torch

import torch.nn as nn

import torch.optim as optim

import torch.nn.functional as F

from torch.distributions.categorical import Categorical

from torch.utils.tensorboard import SummaryWriter

import argparse

from distutils.util import strtobool

import numpy as np

import gym

import gym_microrts

from gym.wrappers import TimeLimit, Monitor

from gym.spaces import Discrete, Box, MultiBinary, MultiDiscrete, Space

import time

import random

import os

from stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv, VecEnvWrapper

if __name__ == "__main__":

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

# Common arguments

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="MicrortsCombinedReward10x10F9BuildCombatUnits-v0",

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 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=0.1,

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('--kle-rollback', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,

help='If toggled, the policy updates will roll back to previous policy if KL exceeds 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)

class ImageToPyTorch(gym.ObservationWrapper):

def __init__(self, env):

super(ImageToPyTorch, self).__init__(env)

old_shape = self.observation_space.shape

self.observation_space = gym.spaces.Box(

low=0,

high=1,

shape=(old_shape[-1], old_shape[0], old_shape[1]),

dtype=np.int32,

)

def observation(self, observation):

return np.transpose(observation, axes=(2, 0, 1))

class VecPyTorch(VecEnvWrapper):

def __init__(self, venv, device):

super(VecPyTorch, self).__init__(venv)

self.device = device

def reset(self):

obs = self.venv.reset()

obs = torch.from_numpy(obs).float().to(self.device)

return obs

def step_async(self, actions):

actions = actions.cpu().numpy()

self.venv.step_async(actions)

def step_wait(self):

obs, reward, done, info = self.venv.step_wait()

obs = torch.from_numpy(obs).float().to(self.device)

reward = torch.from_numpy(reward).unsqueeze(dim=1).float()

return obs, reward, done, info

class MicroRTSStatsRecorder(gym.Wrapper):

def reset(self, **kwargs):

observation = super(MicroRTSStatsRecorder, self).reset(**kwargs)

self.raw_rewards = []

return observation

def step(self, action):

observation, reward, done, info = super(MicroRTSStatsRecorder, self).step(action)

self.raw_rewards += [info["raw_rewards"]]

if done:

raw_rewards = np.array(self.raw_rewards).sum(0)

raw_names = [str(rf) for rf in self.rfs]

info['microrts_stats'] = dict(zip(raw_names, raw_rewards))

self.raw_rewards = []

return observation, reward, done, info

# TRY NOT TO MODIFY: setup the environment

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

writer = SummaryWriter(f"runs/{experiment_name}")

writer.add_text('hyperparameters', "|param|value|\n|-|-|\n%s" % (

'\n'.join([f"|{key}|{value}|" for key, value in vars(args).items()])))

if args.prod_mode:

import wandb

run = wandb.init(project=args.wandb_project_name, entity=args.wandb_entity, sync_tensorboard=True, config=vars(args), name=experiment_name, monitor_gym=True, save_code=True)

writer = SummaryWriter(f"/tmp/{experiment_name}")

# TRY NOT TO MODIFY: seeding

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

random.seed(args.seed)

np.random.seed(args.seed)

torch.manual_seed(args.seed)

torch.backends.cudnn.deterministic = args.torch_deterministic

def make_env(gym_id, seed, idx):

def thunk():

env = gym.make(gym_id)

env = ImageToPyTorch(env)

env = gym.wrappers.RecordEpisodeStatistics(env)

env = MicroRTSStatsRecorder(env)

if args.capture_video:

if idx == 0:

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

env.seed(seed)

env.action_space.seed(seed)

env.observation_space.seed(seed)

return env

return thunk

envs = VecPyTorch(DummyVecEnv([make_env(args.gym_id, args.seed+i, i) for i in range(args.num_envs)]), device)

# if args.prod_mode:

# envs = VecPyTorch(

# SubprocVecEnv([make_env(args.gym_id, args.seed+i, i) for i in range(args.num_envs)], "fork"),

# device

# )

assert isinstance(envs.action_space, MultiDiscrete), "only MultiDiscrete action space is supported"

# ALGO LOGIC: initialize agent here:

class CategoricalMasked(Categorical):

def __init__(self, probs=None, logits=None, validate_args=None, masks=[]):

self.masks = masks

if len(self.masks) == 0:

super(CategoricalMasked, self).__init__(probs, logits, validate_args)

else:

self.masks = masks.type(torch.BoolTensor).to(device)

logits = torch.where(self.masks, logits, torch.tensor(-1e+8).to(device))

super(CategoricalMasked, self).__init__(probs, logits, validate_args)

def entropy(self):

if len(self.masks) == 0:

return super(CategoricalMasked, self).entropy()

p_log_p = self.logits * self.probs

p_log_p = torch.where(self.masks, p_log_p, torch.tensor(0.).to(device))

return -p_log_p.sum(-1)

class Scale(nn.Module):

def __init__(self, scale):

super().__init__()

self.scale = scale

def forward(self, x):

return x * self.scale

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, frames=4):

super(Agent, self).__init__()

self.network = nn.Sequential(

layer_init(nn.Conv2d(27, 16, kernel_size=3, stride=2)),

nn.ReLU(),

layer_init(nn.Conv2d(16, 32, kernel_size=2)),

nn.ReLU(),

nn.Flatten(),

layer_init(nn.Linear(32*3*3, 128)),

nn.ReLU(),)

self.actor = layer_init(nn.Linear(128, envs.action_space.nvec.sum()), std=0.01)

self.critic = layer_init(nn.Linear(128, 1), std=1)

def forward(self, x):

return self.network(x)

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

def get_action(self, x, action=None, invalid_action_masks=None, envs=None):

logits = self.actor(self.forward(x))

split_logits = torch.split(logits, envs.action_space.nvec.tolist(), dim=1)

multi_categoricals = [Categorical(logits=logits) for logits in split_logits]

if action is None:

# 1. select source unit based on source unit mask

source_unit_mask = torch.Tensor(np.array(envs.env_method("get_unit_location_mask", player=1)))

multi_categoricals = [CategoricalMasked(logits=split_logits[0], masks=source_unit_mask)]

action_components = [multi_categoricals[0].sample()]

# 2. select action type and parameter section based on the

# source-unit mask of action type and parameters

source_unit_action_mask = torch.Tensor(

[envs.env_method("get_unit_action_mask", unit=action_components[0][i], player=1, indices=i)[0]

for i in range(envs.num_envs)])

split_suam = torch.split(source_unit_action_mask, envs.action_space.nvec.tolist()[1:], dim=1)

multi_categoricals = multi_categoricals + [CategoricalMasked(logits=logits, masks=iam) for (logits, iam) in zip(split_logits[1:], split_suam)]

invalid_action_masks = torch.cat((source_unit_mask, source_unit_action_mask), 1)

action = torch.stack([categorical.sample() for categorical in multi_categoricals])

else:

split_invalid_action_masks = torch.split(invalid_action_masks, envs.action_space.nvec.tolist(), dim=1)

multi_categoricals = [CategoricalMasked(logits=logits, masks=iam) for (logits, iam) in zip(split_logits, split_invalid_action_masks)]

logprob = torch.stack([categorical.log_prob(a) for a, categorical in zip(action, multi_categoricals)])

entropy = torch.stack([categorical.entropy() for categorical in multi_categoricals])

return action, logprob.sum(0), entropy.sum(0)

return action, logprob.sum(0), entropy.sum(0), invalid_action_masks

def get_value(self, x):

return self.critic(self.forward(x))

agent = Agent().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)

invalid_action_masks = torch.zeros((args.num_steps, args.num_envs) + (envs.action_space.nvec.sum(),)).to(device)

# TRY NOT TO MODIFY: start the game

global_step = 0

# 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 = envs.reset()

next_done = torch.zeros(args.num_envs).to(device)

num_updates = args.total_timesteps // args.batch_size

## CRASH AND RESUME LOGIC:

starting_update = 1

if args.prod_mode and wandb.run.resumed:

print("previous run.summary", run.summary)

starting_update = run.summary['charts/update'] + 1

global_step = starting_update * args.batch_size

api = wandb.Api()

run = api.run(run.get_url()[len("https://app.wandb.ai/"):])

model = run.file('agent.pt')

model.download(f"models/{experiment_name}/")

agent.load_state_dict(torch.load(f"models/{experiment_name}/agent.pt"))

agent.eval()

print(f"resumed at update {starting_update}")

for update in range(starting_update, 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):

envs.env_method("render", indices=0)

global_step += 1 * args.num_envs

obs[step] = next_obs

dones[step] = next_done

# ALGO LOGIC: put action logic here

with torch.no_grad():

values[step] = agent.get_value(obs[step]).flatten()

action, logproba, _ = agent.get_action(obs[step])

action, logproba, _, invalid_action_masks[step] = agent.get_action(obs[step], envs=envs)

actions[step] = action.T

logprobs[step] = logproba

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

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

rewards[step], next_done = rs.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)

for key in info['microrts_stats']:

writer.add_scalar(f"charts/episode_reward/{key}", info['microrts_stats'][key], 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)

b_invalid_action_masks = invalid_action_masks.reshape((-1, invalid_action_masks.shape[-1]))

# Optimizaing the policy and value network

target_agent = Agent().to(device)

inds = np.arange(args.batch_size,)

for i_epoch_pi in range(args.update_epochs):

np.random.shuffle(inds)

target_agent.load_state_dict(agent.state_dict())

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 = agent.get_action(

_, newlogproba, entropy, _ = agent.get_action(

b_obs[minibatch_ind],

b_actions.long()[minibatch_ind].T)

b_actions.long()[minibatch_ind].T,

b_invalid_action_masks[minibatch_ind],

envs)

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

# Stats

approx_kl = (b_logprobs[minibatch_ind] - newlogproba).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 = agent.get_value(b_obs[minibatch_ind]).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)

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

if args.kle_rollback:

if (b_logprobs[minibatch_ind] - agent.get_action(

b_obs[minibatch_ind],

b_actions.long()[minibatch_ind].T)[1]).mean() > args.target_kl:

b_actions.long()[minibatch_ind].T,

b_invalid_action_masks[minibatch_ind],

envs)[1]).mean() > args.target_kl:

agent.load_state_dict(target_agent.state_dict())

break

## CRASH AND RESUME LOGIC:

if args.prod_mode:

if not os.path.exists(f"models/{experiment_name}"):

os.makedirs(f"models/{experiment_name}")

torch.save(agent.state_dict(), f"{wandb.run.dir}/agent.pt")

wandb.save(f"agent.pt")

# 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("charts/update", update, 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)

if args.kle_stop or args.kle_rollback:

writer.add_scalar("debug/pg_stop_iter", i_epoch_pi, global_step)

envs.close()

writer.close()

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

import argparse
from distutils.util import strtobool
import numpy as np
import gym
import gym_microrts
from gym.wrappers import TimeLimit, Monitor

from gym.spaces import Discrete, Box, MultiBinary, MultiDiscrete, Space
import time
import random
import os
from stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv, VecEnvWrapper

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='PPO agent')
    # Common arguments
    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="MicrortsCombinedReward10x10F9BuildCombatUnits-v0",
                        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 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=0.1,
                        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('--kle-rollback', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
                         help='If toggled, the policy updates will roll back to previous policy if KL exceeds 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)

class ImageToPyTorch(gym.ObservationWrapper):
    def __init__(self, env):
        super(ImageToPyTorch, self).__init__(env)
        old_shape = self.observation_space.shape
        self.observation_space = gym.spaces.Box(
            low=0,
            high=1,
            shape=(old_shape[-1], old_shape[0], old_shape[1]),
            dtype=np.int32,
        )

def observation(self, observation):
        return np.transpose(observation, axes=(2, 0, 1))

class VecPyTorch(VecEnvWrapper):
    def __init__(self, venv, device):
        super(VecPyTorch, self).__init__(venv)
        self.device = device

def reset(self):
        obs = self.venv.reset()
        obs = torch.from_numpy(obs).float().to(self.device)
        return obs

def step_async(self, actions):
        actions = actions.cpu().numpy()
        self.venv.step_async(actions)

def step_wait(self):
        obs, reward, done, info = self.venv.step_wait()
        obs = torch.from_numpy(obs).float().to(self.device)
        reward = torch.from_numpy(reward).unsqueeze(dim=1).float()
        return obs, reward, done, info

class MicroRTSStatsRecorder(gym.Wrapper):

def reset(self, **kwargs):
        observation = super(MicroRTSStatsRecorder, self).reset(**kwargs)
        self.raw_rewards = []
        return observation

def step(self, action):
        observation, reward, done, info = super(MicroRTSStatsRecorder, self).step(action)
        self.raw_rewards += [info["raw_rewards"]]
        if done:
            raw_rewards = np.array(self.raw_rewards).sum(0)
            raw_names = [str(rf) for rf in self.rfs]
            info['microrts_stats'] = dict(zip(raw_names, raw_rewards))
            self.raw_rewards = []
        return observation, reward, done, info

# TRY NOT TO MODIFY: setup the environment
experiment_name = f"{args.gym_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
writer = SummaryWriter(f"runs/{experiment_name}")
writer.add_text('hyperparameters', "|param|value|\n|-|-|\n%s" % (
        '\n'.join([f"|{key}|{value}|" for key, value in vars(args).items()])))
if args.prod_mode:
    import wandb
    run = wandb.init(project=args.wandb_project_name, entity=args.wandb_entity, sync_tensorboard=True, config=vars(args), name=experiment_name, monitor_gym=True, save_code=True)
    writer = SummaryWriter(f"/tmp/{experiment_name}")

# TRY NOT TO MODIFY: seeding
device = torch.device('cuda' if torch.cuda.is_available() and args.cuda else 'cpu')
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.backends.cudnn.deterministic = args.torch_deterministic
def make_env(gym_id, seed, idx):
    def thunk():
        env = gym.make(gym_id)
        env = ImageToPyTorch(env)
        env = gym.wrappers.RecordEpisodeStatistics(env)
        env = MicroRTSStatsRecorder(env)
        if args.capture_video:
            if idx == 0:
                env = Monitor(env, f'videos/{experiment_name}')
        env.seed(seed)
        env.action_space.seed(seed)
        env.observation_space.seed(seed)
        return env
    return thunk
envs = VecPyTorch(DummyVecEnv([make_env(args.gym_id, args.seed+i, i) for i in range(args.num_envs)]), device)
# if args.prod_mode:
#     envs = VecPyTorch(
#         SubprocVecEnv([make_env(args.gym_id, args.seed+i, i) for i in range(args.num_envs)], "fork"),
#         device
#     )
assert isinstance(envs.action_space, MultiDiscrete), "only MultiDiscrete action space is supported"

# ALGO LOGIC: initialize agent here:
class Scale(nn.Module):
    def __init__(self, scale):
        super().__init__()
        self.scale = scale

def forward(self, x):
        return x * self.scale

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, frames=4):
        super(Agent, self).__init__()
        self.network = nn.Sequential(
            layer_init(nn.Conv2d(27, 16, kernel_size=3, stride=2)),
            nn.ReLU(),
            layer_init(nn.Conv2d(16, 32, kernel_size=2)),
            nn.ReLU(),
            nn.Flatten(),
            layer_init(nn.Linear(32*3*3, 128)),
            nn.ReLU(),)
        self.actor = layer_init(nn.Linear(128, envs.action_space.nvec.sum()), std=0.01)
        self.critic = layer_init(nn.Linear(128, 1), std=1)

def forward(self, x):
        return self.network(x)

def get_action(self, x, action=None):
        logits = self.actor(self.forward(x))
        split_logits = torch.split(logits, envs.action_space.nvec.tolist(), dim=1)
        multi_categoricals = [Categorical(logits=logits) for logits in split_logits]
        if action is None:
            action = torch.stack([categorical.sample() for categorical in multi_categoricals])
        logprob = torch.stack([categorical.log_prob(a) for a, categorical in zip(action, multi_categoricals)])
        entropy = torch.stack([categorical.entropy() for categorical in multi_categoricals])
        return action, logprob.sum(0), entropy.sum(0)

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

agent = Agent().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
# 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 = envs.reset()
next_done = torch.zeros(args.num_envs).to(device)
num_updates = args.total_timesteps // args.batch_size

## CRASH AND RESUME LOGIC:
starting_update = 1
if args.prod_mode and wandb.run.resumed:
    print("previous run.summary", run.summary)
    starting_update = run.summary['charts/update'] + 1
    global_step = starting_update * args.batch_size
    api = wandb.Api()
    run = api.run(run.get_url()[len("https://app.wandb.ai/"):])
    model = run.file('agent.pt')
    model.download(f"models/{experiment_name}/")
    agent.load_state_dict(torch.load(f"models/{experiment_name}/agent.pt"))
    agent.eval()
    print(f"resumed at update {starting_update}")
for update in range(starting_update, 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):
        envs.env_method("render", indices=0)
        global_step += 1 * args.num_envs
        obs[step] = next_obs
        dones[step] = next_done

# ALGO LOGIC: put action logic here
        with torch.no_grad():
            values[step] = agent.get_value(obs[step]).flatten()
            action, logproba, _ = agent.get_action(obs[step])
        
        actions[step] = action.T
        logprobs[step] = logproba

# TRY NOT TO MODIFY: execute the game and log data.
        next_obs, rs, ds, infos = envs.step(action.T)
        rewards[step], next_done = rs.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)
                for key in info['microrts_stats']:
                    writer.add_scalar(f"charts/episode_reward/{key}", info['microrts_stats'][key], 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
    target_agent = Agent().to(device)
    inds = np.arange(args.batch_size,)
    for i_epoch_pi in range(args.update_epochs):
        np.random.shuffle(inds)
        target_agent.load_state_dict(agent.state_dict())
        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 = agent.get_action(
                b_obs[minibatch_ind],
                b_actions.long()[minibatch_ind].T)
            ratio = (newlogproba - b_logprobs[minibatch_ind]).exp()

# Stats
            approx_kl = (b_logprobs[minibatch_ind] - newlogproba).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 = agent.get_value(b_obs[minibatch_ind]).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)

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
        if args.kle_rollback:
            if (b_logprobs[minibatch_ind] - agent.get_action(
                    b_obs[minibatch_ind],
                    b_actions.long()[minibatch_ind].T)[1]).mean() > args.target_kl:
                agent.load_state_dict(target_agent.state_dict())
                break

## CRASH AND RESUME LOGIC:
    if args.prod_mode:
        if not os.path.exists(f"models/{experiment_name}"):
            os.makedirs(f"models/{experiment_name}")
        torch.save(agent.state_dict(), f"{wandb.run.dir}/agent.pt")
        wandb.save(f"agent.pt")

# 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("charts/update", update, 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)
    if args.kle_stop or args.kle_rollback:
        writer.add_scalar("debug/pg_stop_iter", i_epoch_pi, global_step)

envs.close()
writer.close()

Texto modificado