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‎.gitignore

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__pycache__/
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.idea/

‎Pong-v0_monitor/openaigym.episode_batch.0.3438.stats.json

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{"initial_reset_timestamp": 1501897257.560941, "timestamps": [1501897419.795865, 1501897524.366334, 1501897667.15831], "episode_lengths": [9679, 6266, 8561], "episode_rewards": [-1.0, 11.0, -6.0], "episode_types": ["t", "t", "t", "t"]}

‎Pong-v0_monitor/openaigym.manifest.0.3438.manifest.json

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{"stats": "openaigym.episode_batch.0.3438.stats.json", "videos": [["openaigym.video.0.3438.video000000.mp4", "openaigym.video.0.3438.video000000.meta.json"], ["openaigym.video.0.3438.video000001.mp4", "openaigym.video.0.3438.video000001.meta.json"]], "env_info": {"gym_version": "0.9.2", "env_id": "Pong-v0"}}

‎Pong-v0_monitor/openaigym.video.0.3438.video000000.meta.json

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{"episode_id": 0, "content_type": "video/mp4", "encoder_version": {"backend": "ffmpeg", "version": "b'ffmpeg version 3.2.4 Copyright (c) 2000-2017 the FFmpeg developers\\nbuilt with Apple LLVM version 8.0.0 (clang-800.0.42.1)\\nconfiguration: --prefix=/usr/local/Cellar/ffmpeg/3.2.4 --enable-shared --enable-pthreads --enable-gpl --enable-version3 --enable-hardcoded-tables --enable-avresample --cc=clang --host-cflags= --host-ldflags= --enable-libmp3lame --enable-libx264 --enable-libxvid --enable-opencl --disable-lzma --enable-vda\\nlibavutil 55. 34.101 / 55. 34.101\\nlibavcodec 57. 64.101 / 57. 64.101\\nlibavformat 57. 56.101 / 57. 56.101\\nlibavdevice 57. 1.100 / 57. 1.100\\nlibavfilter 6. 65.100 / 6. 65.100\\nlibavresample 3. 1. 0 / 3. 1. 0\\nlibswscale 4. 2.100 / 4. 2.100\\nlibswresample 2. 3.100 / 2. 3.100\\nlibpostproc 54. 1.100 / 54. 1.100\\n'", "cmdline": ["ffmpeg", "-nostats", "-loglevel", "error", "-y", "-r", "30", "-f", "rawvideo", "-s:v", "160x210", "-pix_fmt", "rgb24", "-i", "-", "-vcodec", "libx264", "-pix_fmt", "yuv420p", "/Volumes/xs/CodeSpace/AISpace/rl_space/rl_atari_pytorch/Pong-v0_monitor/openaigym.video.0.3438.video000000.mp4"]}}

‎Pong-v0_monitor/openaigym.video.0.3438.video000000.mp4

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‎Pong-v0_monitor/openaigym.video.0.3438.video000001.meta.json

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{"episode_id": 1, "content_type": "video/mp4", "encoder_version": {"backend": "ffmpeg", "version": "b'ffmpeg version 3.2.4 Copyright (c) 2000-2017 the FFmpeg developers\\nbuilt with Apple LLVM version 8.0.0 (clang-800.0.42.1)\\nconfiguration: --prefix=/usr/local/Cellar/ffmpeg/3.2.4 --enable-shared --enable-pthreads --enable-gpl --enable-version3 --enable-hardcoded-tables --enable-avresample --cc=clang --host-cflags= --host-ldflags= --enable-libmp3lame --enable-libx264 --enable-libxvid --enable-opencl --disable-lzma --enable-vda\\nlibavutil 55. 34.101 / 55. 34.101\\nlibavcodec 57. 64.101 / 57. 64.101\\nlibavformat 57. 56.101 / 57. 56.101\\nlibavdevice 57. 1.100 / 57. 1.100\\nlibavfilter 6. 65.100 / 6. 65.100\\nlibavresample 3. 1. 0 / 3. 1. 0\\nlibswscale 4. 2.100 / 4. 2.100\\nlibswresample 2. 3.100 / 2. 3.100\\nlibpostproc 54. 1.100 / 54. 1.100\\n'", "cmdline": ["ffmpeg", "-nostats", "-loglevel", "error", "-y", "-r", "30", "-f", "rawvideo", "-s:v", "160x210", "-pix_fmt", "rgb24", "-i", "-", "-vcodec", "libx264", "-pix_fmt", "yuv420p", "/Volumes/xs/CodeSpace/AISpace/rl_space/rl_atari_pytorch/Pong-v0_monitor/openaigym.video.0.3438.video000001.mp4"]}}

‎Pong-v0_monitor/openaigym.video.0.3438.video000001.mp4

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‎checkpoints/Pong-v0.model

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‎config.json

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{
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"Default": {
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"crop1": 34,
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"crop2": 34,
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"dimension2": 80
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},
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"Asteroids": {
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"crop1": 16,
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"crop2": 34,
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"dimension2": 94
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},
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"BeamRider": {
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"crop1": 20,
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"crop2": 20,
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"dimension2": 80
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},
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"Breakout": {
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"crop1": 34,
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"crop2": 34,
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"dimension2": 80
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},
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"Centipede": {
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"crop1": 36,
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"crop2": 56,
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"dimension2": 90
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},
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"MsPacman": {
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"crop1": 2,
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"crop2": 10,
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"dimension2": 84
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},
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"Pong": {
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"crop1": 34,
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"crop2": 34,
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"dimension2": 80
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},
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"Seaquest": {
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"crop1": 30,
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"crop2": 30,
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"dimension2": 80
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},
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"SpaceInvaders": {
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"crop1": 8,
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"crop2": 36,
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"dimension2": 94
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},
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"VideoPinball": {
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"crop1": 42,
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"crop2": 60,
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"dimension2": 89
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}
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}

‎environment.py

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from __future__ import division
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import gym
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import numpy as np
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from gym.spaces.box import Box
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from universe import vectorized
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from universe.wrappers import Unvectorize, Vectorize
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from skimage.color import rgb2gray
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import cv2
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def atari_env(env_id, env_conf):
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env = gym.make(env_id)
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if len(env.observation_space.shape) > 1:
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env = Vectorize(env)
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env = AtariRescale(env, env_conf)
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env = NormalizedEnv(env)
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env = Unvectorize(env)
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return env
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def _process_frame(frame, conf):
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frame = frame[conf["crop1"]:conf["crop2"] + 160, :160]
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frame = cv2.resize(rgb2gray(frame), (80, conf["dimension2"]))
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frame = cv2.resize(frame, (80, 80))
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frame = np.reshape(frame, [1, 80, 80])
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return frame
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class AtariRescale(vectorized.ObservationWrapper):
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def __init__(self, env, env_conf):
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super(AtariRescale, self).__init__(env)
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self.observation_space = Box(0.0, 1.0, [1, 80, 80])
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self.conf = env_conf
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def _observation(self, observation_n):
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return [
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_process_frame(observation, self.conf)
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for observation in observation_n
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]
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class NormalizedEnv(vectorized.ObservationWrapper):
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def __init__(self, env=None):
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super(NormalizedEnv, self).__init__(env)
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self.state_mean = 0
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self.state_std = 0
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self.alpha = 0.9999
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self.num_steps = 0
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def _observation(self, observation_n):
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for observation in observation_n:
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self.num_steps += 1
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self.state_mean = self.state_mean * self.alpha + \
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observation.mean() * (1 - self.alpha)
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self.state_std = self.state_std * self.alpha + \
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observation.std() * (1 - self.alpha)
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unbiased_mean = self.state_mean / (1 - pow(self.alpha, self.num_steps))
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unbiased_std = self.state_std / (1 - pow(self.alpha, self.num_steps))
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return [(observation - unbiased_mean) / (unbiased_std + 1e-8)
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for observation in observation_n]

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