def set_env(): register( id='SnakeEnv-v0', entry_point='gym.envs.gym_snake:SingleSnake', ) add_group(id='gym_snake', name='gym_snake', description='snake') add_task(id='SnakeEnv-v0', group='gym_snake', summary="Multi snakes environment")
from gym.envs.registration import register from gym.scoreboard.registration import add_task, add_group from .package_info import USERNAME # Environment registration for d in [4, 8, 16, 32, 64]: register( id='{}/gridworld-{}x{}'.format(USERNAME, d, d), entry_point='{}_gym_gridworld:GridWorldEnv'.format(USERNAME), max_episode_steps=1000, reward_threshold=9000.0, kwargs={'dimension': d}, ) # Scoreboard registration # ========================== add_group(id='gridworld', name='GridWorld', description='Sutton & Barto classic Gridworld environment.') for d in [4, 8, 16, 32, 64]: add_task(id='{}/GridWorld-{}x{}'.format(USERNAME, d, d), group='gridworld', summary='Sutton and Barto Grid world, {}x{}.'.format(d), description=""" """)
from gym.scoreboard.registration import registry, add_task, add_group # Discover API key from the environment. (You should never have to # change api_base / web_base.) api_key = os.environ.get('OPENAI_GYM_API_KEY') api_base = os.environ.get('OPENAI_GYM_API_BASE', 'https://gym-api.openai.com') web_base = os.environ.get('OPENAI_GYM_WEB_BASE', 'https://gym.openai.com') # The following controls how various tasks appear on the # scoreboard. These registrations can differ from what's registered in # this repository. # groups add_group(id='classic_control', name='Classic control', description='Classic control problems from the RL literature.') add_group( id='algorithmic', name='Algorithmic', description='Learn to imitate computations.', ) add_group( id='atari', name='Atari', description='Reach high scores in Atari 2600 games.', ) add_group(
import os from gym.scoreboard.registration import registry, add_task, add_group add_group(id='gazebo', name='Gazebo', description='TODO.') add_task( id='test-v0', group='gazebo', summary='Obstacle avoidance in a Circuit.', ) registry.finalize()
# Discover API key from the environment. (You should never have to # change api_base / web_base.) api_key = os.environ.get('OPENAI_GYM_API_KEY') api_base = os.environ.get('OPENAI_GYM_API_BASE', 'https://gym-api.openai.com') web_base = os.environ.get('OPENAI_GYM_WEB_BASE', 'https://gym.openai.com') # The following controls how various tasks appear on the # scoreboard. These registrations can differ from what's registered in # this repository. # groups add_group( id='classic_control', name='Classic control', description='Classic control problems from the RL literature.' ) add_group( id='algorithmic', name='Algorithmic', description='Learn to imitate computations.', ) add_group( id='atari', name='Atari', description='Reach high scores in Atari 2600 games.', )
entry_point='{}_gym_doom:DoomTakeCoverEnv'.format(USERNAME), max_episode_steps=10000, reward_threshold=750.0, ) register( id='{}/DoomDeathmatch-v0'.format(USERNAME), entry_point='{}_gym_doom:DoomDeathmatchEnv'.format(USERNAME), max_episode_steps=10000, reward_threshold=20.0, ) # Scoreboard registration # ========================== add_group(id='doom', name='Doom', description='Doom environments based on VizDoom.') add_task(id='{}/meta-Doom-v0'.format(USERNAME), group='doom', summary='Mission #1 to #9 - Beat all 9 Doom missions.', description=""" This is a meta map that combines all 9 Doom levels. Levels: - #0 Doom Basic - #1 Doom Corridor - #2 Doom DefendCenter - #3 Doom DefendLine - #4 Doom HealthGathering - #5 Doom MyWayHome
from gym.scoreboard.registration import add_task, add_group add_group(id='bandits', name='Bandits', description='Various multi-armed Bandit environments') add_task( id='multi_arm_bandit_gaussian_fixed-v0', group='bandits', experimental=True, contributor='bardofcodes', summary= "multi-armed bandit mentioned with reward based on a Gaussian distribution", description=""" Each bandit gives a fixed reward,'r', where 'r' is sampled from a Gaussian Distribution(5,2) """, background="") add_task( id='multi_arm_bandit_gaussian_gaussian-v0', group='bandits', experimental=True, contributor='bardofcodes', summary= "multi-armed bandit with each bandit having a normal distribution for reward distribution", description=""" Each bandit has a N(r,1) reward distribution, where 'r' is sampled from a Normal(5,2) distribution. """, background="") add_task(
level = (world_number - 1) * 4 + (level_number - 1) register( id='{}/SuperMarioBros-{}-{}{}-v0'.format(USERNAME, world_number, level_number, tile_suffix), entry_point='{}_gym_super_mario:SuperMarioBrosEnv'.format(USERNAME), max_episode_steps=10000, reward_threshold=(max_distance - 40), kwargs={ 'draw_tiles': draw_tiles, 'level': level }, # Seems to be non-deterministic about 5% of the time nondeterministic=True, ) # Scoreboard registration # ========================== add_group( id= 'super-mario', name= 'SuperMario', description= '32 levels of the original Super Mario Bros game.' ) add_task( id='{}/meta-SuperMarioBros-v0'.format(USERNAME), group='super-mario', summary='Compilation of all 32 levels of Super Mario Bros. on Nintendo platform - Screen version.', ) add_task( id='{}/meta-SuperMarioBros-Tiles-v0'.format(USERNAME), group='super-mario', summary='Compilation of all 32 levels of Super Mario Bros. on Nintendo platform - Tiles version.', ) for world in range(8):
) register( id='vladfi1/SSBM-headless-v0', entry_point='dolphin:simpleSSBMEnv', reward_threshold=1, timestep_limit=9999999, kwargs=dict( cpu=9, stage='battlefield', ), nondeterministic=True, ) # Scoreboard registration # ========================== add_group( id= 'ssbm', name= 'Super Smash Bros. Melee', description= 'Beat the in-game AIs at SSBM.' ) """ add_task( id='{}/meta-SuperMarioBros-v0'.format(USERNAME), group='ssbm', summary='Compilation of all 32 levels of Super Mario Bros. on Nintendo platform - Screen version.', ) """
import gym from gym.envs.registration import register from gym.scoreboard.registration import add_group from gym.scoreboard.registration import add_taskcd register( id='SuperMarioBros-1-1-v0', entry_point='gym.envs.ppaquette_gym_super_mario:MetaSuperMarioBrosEnv', ) add_group(id='ppaquette_gym_super_mario', name='ppaquette_gym_super_mario', description='super_mario') add_task(id='SuperMarioBros-1-1-v0', group='ppaquette_gym_super_mario', summary="SuperMarioBros-1-1-v0") env = gym.make('SuperMarioBros-1-1-v0') env.reset() for _ in range(10000): env.render() env.step(env.action_space.sample()) # take a random action
from gym.scoreboard.registration import add_task, add_group add_group(id='SpaceFortress', name='SpaceFortress', description='SpaceFortress games') add_task(id='SpaceFortress-explode-image-v0', summary="2D frictionless space shooter (explode, image)", group='SpaceFortress', contributor='Ryan M. Hope') add_task(id='SpaceFortress-autoturn-image-v0', summary="2D frictionless space shooter (autoturn, image)", group='SpaceFortress', contributor='Ryan M. Hope') add_task(id='SpaceFortress-explode-features-v0', summary="2D frictionless space shooter (explode, features)", group='SpaceFortress', contributor='Ryan M. Hope') add_task(id='SpaceFortress-autoturn-features-v0', summary="2D frictionless space shooter (autoturn, features)", group='SpaceFortress', contributor='Ryan M. Hope')
from gym.scoreboard.registration import registry, add_task, add_group # Discover API key from the environment. (You should never have to # change api_base / web_base.) '''api_key = os.environ.get('OPENAI_GYM_API_KEY') api_base = os.environ.get('OPENAI_GYM_API_BASE', 'https://gym-api.openai.com') web_base = os.environ.get('OPENAI_GYM_WEB_BASE', 'https://gym.openai.com')''' # The following controls how various tasks appear on the # scoreboard. These registrations can differ from what's registered in # this repository. # groups add_group(id='gym-vehicle', name='gym-vehicle', description='TODO.') add_task( id='GazeboCircuitLargeCatvehicleLidar-v0', group='gym-vehicle', summary='Test1.', ) add_task( id='GazeboCircuitLargeCatvehicleLidarNn-v0', group='gym-vehicle', summary='Test2.', ) add_task( id='GazeboTrackCatvehicleLidar-v0',
#tags={'wrapper_config.TimeLimit.max_episode_steps': 1818}, #timestep_limit=1818, reward_threshold=16000) register( id='MinecraftHard-v0', entry_point='gym_minecraft.envs:MinecraftEnv', kwargs={'mission_file': 'hard.xml'}, #tags={'wrapper_config.TimeLimit.max_episode_steps': 2424}, #timestep_limit=2424, reward_threshold=32000) # Scoreboard registration # ========================== add_group(id='minecraft', name='Minecraft', description='Minecraft environments based on Malmo.') add_task(id='MinecraftDefaultWorld1-v0', group='minecraft', summary='Survive and find gold, diamond or redstone!', description=""" The agent appears in a default Minecraft world, with all possible objects. The agent appears at x="-204" y="81" z="217", which depending on the world that is generated means that it's going to fall initially to touch ground. Goal: The task instance is considered complete if the agent finds (mines) any special block (any of "gold_block diamond_block redstone_block"). Rewards:
max_episode_steps=10000, reward_threshold=20.0, ) register( id='{}/DoomBattle3-v0', entry_point='{}.gym.doom:DoomBattleD3Env', max_episode_steps=10000, reward_threshold=20.0, ) # Scoreboard registration # ========================== add_group( id= 'doom', name= 'Doom', description= 'Doom environments based on VizDoom.' ) add_task( id='{}/meta-Doom-v0', group='doom', summary='Mission #1 to #9 - Beat all 9 Doom missions.', description=""" This is a meta map that combines all 9 Doom levels. Levels: - #0 Doom Basic - #1 Doom Corridor - #2 Doom DefendCenter - #3 Doom DefendLine