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Running on Zero
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warnings.filterwarnings('ignore')
import gymnasium as gym
import numpy as np
import ale_py
from envs.wrappers.timeout import Timeout
ATARI_TASKS = {
'atari-alien': dict(
env='ALE/Alien-v5',
min_return=0,
max_return=1000,
),
'atari-assault': dict(
env='ALE/Assault-v5',
min_return=0,
max_return=1000,
),
'atari-asterix': dict(
env='ALE/Asterix-v5',
min_return=0,
max_return=5000,
),
'atari-atlantis': dict(
env='ALE/Atlantis-v5',
min_return=0,
max_return=50000,
),
'atari-bank-heist': dict(
env='ALE/BankHeist-v5',
min_return=0,
max_return=1000,
),
'atari-battle-zone': dict(
env='ALE/BattleZone-v5',
min_return=0,
max_return=15000,
),
'atari-beamrider': dict(
env='ALE/BeamRider-v5',
min_return=0,
max_return=2000,
),
'atari-berzerk': dict(
env='ALE/Berzerk-v5',
min_return=0,
max_return=500,
),
'atari-bowling': dict(
env='ALE/Bowling-v5',
min_return=0,
max_return=100,
),
'atari-boxing': dict(
env='ALE/Boxing-v5',
min_return=-100,
max_return=100,
),
'atari-chopper-command': dict(
env='ALE/ChopperCommand-v5',
min_return=0,
max_return=1000,
),
'atari-crazy-climber': dict(
env='ALE/CrazyClimber-v5',
min_return=0,
max_return=20000,
),
'atari-double-dunk': dict(
env='ALE/DoubleDunk-v5',
min_return=0,
max_return=25,
),
'atari-enduro': dict(
env='ALE/Enduro-v5',
min_return=0,
max_return=2500,
),
'atari-fishing-derby': dict(
env='ALE/FishingDerby-v5',
min_return=0,
max_return=80,
),
'atari-gopher': dict(
env='ALE/Gopher-v5',
min_return=0,
max_return=2000,
),
'atari-gravitar': dict(
env='ALE/Gravitar-v5',
min_return=0,
max_return=5000,
),
'atari-ice-hockey': dict(
env='ALE/IceHockey-v5',
min_return=-15,
max_return=15,
),
'atari-jamesbond': dict(
env='ALE/Jamesbond-v5',
min_return=0,
max_return=1000,
),
'atari-kangaroo': dict(
env='ALE/Kangaroo-v5',
min_return=0,
max_return=10000,
),
'atari-krull': dict(
env='ALE/Krull-v5',
min_return=0,
max_return=10000,
),
'atari-ms-pacman': dict(
env='ALE/MsPacman-v5',
min_return=0,
max_return=5000,
),
'atari-name-this-game': dict(
env='ALE/NameThisGame-v5',
min_return=0,
max_return=3000,
),
'atari-phoenix': dict(
env='ALE/Phoenix-v5',
min_return=0,
max_return=1000,
),
'atari-pong': dict(
env='ALE/Pong-v5',
min_return=-21,
max_return=21,
),
'atari-riverraid': dict(
env='ALE/Riverraid-v5',
min_return=0,
max_return=5000,
),
'atari-road-runner': dict(
env='ALE/RoadRunner-v5',
min_return=0,
max_return=50000,
),
'atari-robotank': dict(
env='ALE/Robotank-v5',
min_return=0,
max_return=50,
),
'atari-seaquest': dict(
env='ALE/Seaquest-v5',
min_return=0,
max_return=5000,
),
'atari-space-invaders': dict(
env='ALE/SpaceInvaders-v5',
min_return=0,
max_return=2000,
),
'atari-tennis': dict(
env='ALE/Tennis-v5',
min_return=-21,
max_return=21,
),
'atari-tutankham': dict(
env='ALE/Tutankham-v5',
min_return=0,
max_return=100,
),
'atari-upndown': dict(
env='ALE/UpNDown-v5',
min_return=0,
max_return=5000,
),
'atari-wizard-of-wor': dict(
env='ALE/WizardOfWor-v5',
min_return=0,
max_return=5000,
),
'atari-yars-revenge': dict(
env='ALE/YarsRevenge-v5',
min_return=0,
max_return=20000,
),
}
class AtariWrapper(gym.Wrapper):
def __init__(self, env, cfg):
super().__init__(env)
self.env = env
self.cfg = cfg
if cfg.obs == 'rgb':
self.observation_space = gym.spaces.Dict({
'rgb': gym.spaces.Box(
low=0, high=255, shape=(3, self.cfg.render_size, self.cfg.render_size), dtype=np.uint8),
'state': gym.spaces.Box(
low=-np.inf, high=np.inf, shape=(128,), dtype=np.float32)
})
else:
self.observation_space = gym.spaces.Box(
low=-np.inf, high=np.inf, shape=(128,), dtype=np.float32)
# Actions are radius, theta, and fire, where first two are the parameters of polar coordinates.
self.action_space = gym.spaces.Box(
low=-1.0, high=1.0, shape=(3,), dtype=np.float32)
self._cumulative_reward = 0
self._min_return = ATARI_TASKS[cfg.task]['min_return']
self._max_return = ATARI_TASKS[cfg.task]['max_return']
self._canvas = np.zeros((224, 224, 3), dtype=np.uint8)
def _extract_info(self, info):
info = {
'terminated': info.get('terminated', False),
'truncated': info.get('truncated', False),
'success': float(info.get('success', 0.)),
}
info['score'] = np.clip(
(self._cumulative_reward - self._min_return) / (self._max_return - self._min_return), 0, 1)
return info
def get_observation(self, obs):
if self.cfg.obs == 'rgb':
return {'state': obs, 'rgb': self.render().transpose(2, 0, 1)}
return obs.astype(np.float32) / 255.
def reset(self):
obs, info = self.env.reset()
self._cumulative_reward = 0
return self.get_observation(obs), self._extract_info(info)
def _map_action(self, action):
# Map action from [-1, 1] to the Atari action space.
low, high = self.env.action_space.low, self.env.action_space.high
return action * (high - low) / 2 + (high + low) / 2
def step(self, action):
action = self._map_action(action)
obs, reward, terminated, truncated, info = self.env.step(action)
terminated = False
self._cumulative_reward += reward
info['terminated'] = terminated
info['truncated'] = truncated
return self.get_observation(obs), reward, terminated, truncated, self._extract_info(info)
@property
def unwrapped(self):
return self.env.unwrapped
def render(self, **kwargs):
frame = self.env.render() # (210, 160, 3)
h, w = self.cfg.render_size, self.cfg.render_size
h_start = (h - frame.shape[0]) // 2
w_start = (w - frame.shape[1]) // 2
self._canvas[h_start:h_start + frame.shape[0], w_start:w_start + frame.shape[1]] = frame
return self._canvas.copy()
def make_env(cfg):
"""
Make Atari environment.
"""
if not cfg.task in ATARI_TASKS:
raise ValueError('Unknown task:', cfg.task)
env = gym.make(
ATARI_TASKS[cfg.task]['env'],
obs_type='ram',
continuous=True,
repeat_action_probability=0,
render_mode='rgb_array',
)
env = AtariWrapper(env, cfg)
env = Timeout(env, max_episode_steps=ATARI_TASKS[cfg.task].get('max_episode_steps', 1_000))
return env
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