# -*- coding: utf-8 -*-
"""play_atari_breakout_v1.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1oInyDSY79BWivrxiFZ8TrNJj3IUmaB0u

# Play Atari games with gym 

This notebook implements a simple way to play Atari games (here Atari Breakout)

by G. Turinici june 2022
"""

# Commented out IPython magic to ensure Python compatibility.
#XVFB will be launched if you run on a server
import sys, os
if 'google.colab' in sys.modules:
#     %tensorflow_version 1.x
if type(os.environ.get("DISPLAY")) is not str or len(os.environ.get("DISPLAY"))==0:
    !bash ../xvfb start
#     %env DISPLAY=:1

# Commented out IPython magic to ensure Python compatibility.
import gym
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# %matplotlib inline
import urllib.request

urllib.request.urlretrieve('http://turinici.com/wp-content/uploads/cours/reinforcement_learning/breakout_rom.zip',
                           'breakout_rom.zip')
!mkdir -p romzip
!mv breakout_rom.zip  romzip
!python -m atari_py.import_roms romzip

#useful commands
#!pip install atari_py
#!pip install gym[atari]
#!pip install gym[all]
##to list available envs
#from gym import envs
#print(envs.registry.all())

#play directly : 
env_test = gym.make("BreakoutDeterministic-v4")

#To start a new game
state0=env_test.reset()

print("state0.shape=",state0.shape)
plt.imshow(state0)

print(env_test.observation_space)
print(env_test.action_space)
print(env_test.reward_range)
print(env_test.spec)
print(env_test.metadata)

#test the game; action meaning : 0=NOOP; 1=FIRE; 2=RIGHT; 3=LEFT
# use this cell as many times as necessary changing the action "1" with another
env_test.step(1)
plt.imshow(env_test.render(mode='rgb_array'))