#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Attempting to solve Atari games using A2C.
@author: Ayman Jabri
"""
import torch
import ptan
from lib import data, utils, model
from tensorboardX import SummaryWriter
if __name__=='__main__':
message = '*'*10 + ' A2C on Atari ' +'*'*10
args = utils.argpars_dqn(message)
params = data.params[args.env]
utils.update_params(params, args)
params.n_envs = max(params.n_envs, 8)
device = 'cuda' if args.cuda else 'cpu'
envs = utils.createEnvs(params, stack_frames=2)
shape = envs[0].observation_space.shape
actions = envs[0].action_space.n
net = model.A2CNet(shape, actions)
net.to(device)
agent = ptan.agent.ActorCriticAgent(net, device=device, apply_softmax=True)
exp_src = ptan.experience.ExperienceSourceFirstLast(envs, agent, params.gamma,steps_count=params.steps)
generator = utils.BatchGenerator(exp_src, params)
mean_monitor = utils.MeanRewardsMonitor(envs[0], net, 'A2C', params.solve_rewards)
@author: ayman
"""
import ptan
import gym
import torch
from tensorboardX import SummaryWriter
from lib import data, utils, model, atari_wrappers
ALGORITHM = 'RAINBOW_DDQN'
GAMES = list(data.params.keys())
if __name__ == '__main__':
args = utils.argpars_dqn()
params = data.params[args.env]
utils.update_params(params, args)
device = 'cuda' if args.cuda else 'cpu'
envs = []
for _ in range(params.n_envs):
env = gym.make(params.env)
env = atari_wrappers.wrap_dqn_light(env, params.frame_stack, args.skip)
if params.seed:
env.seed(params.seed)
envs.append(env)
shape = env.observation_space.shape
actions = env.action_space.n