Created
December 16, 2017 17:04
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Test script to check whether data from the Atari Grand Challenge Dataset works with the Atari Learning Environment
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#!/usr/bin/env python | |
# This assumes the ALE python libraries are in the path | |
# and that the dataset is in the current working directory | |
from __future__ import print_function | |
import sys | |
sys.path.insert(0, 'Arcade-Learning-Environment-0.5.0') | |
import csv | |
import pygame | |
from ale_python_interface import ALEInterface | |
ale = ALEInterface() | |
def reload(ale, seed): | |
ale.setFloat('repeat_action_probability', 0) | |
ale.setInt('random_seed', seed) | |
ale.loadROM("montezuma_revenge.bin") | |
ale.setInt('random_seed', seed) | |
ale.setBool('display_screen', True) | |
ale.setBool('sound', True) | |
ale.reset_game() | |
def evaluate(): | |
with open('atari_v2_release/trajectories/revenge/0.txt') as fp: | |
total_reward = 0 | |
fp.readline() | |
fp.readline() | |
reader = csv.reader(fp) | |
for i, row in enumerate(reader): | |
action = row[-1] | |
reward = ale.act(action) | |
if reward != 0: | |
total_reward += reward | |
print('Non-zero reward', reward, 'at frame', i) | |
if ale.game_over(): | |
print('Game over at frame', i) | |
ale.reset_game() | |
break | |
return total_reward | |
seed = 0 | |
total_reward = 0 | |
reload(ale, seed) | |
total_reward = evaluate() | |
print('Total reward:', total_reward, 'seed:', seed) |
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