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from easydict import EasyDict
import os
import gym
from ding.envs import BaseEnv, DingEnvWrapper
from ding.envs.env_wrappers import MaxAndSkipWrapper, WarpFrameWrapper, ScaledFloatFrameWrapper, FrameStackWrapper, \
EvalEpisodeReturnWrapper, TransposeWrapper, TimeLimitWrapper, FlatObsWrapper, GymToGymnasiumWrapper
from ding.policy import PPOFPolicy
def get_instance_config(env_id: str, algorithm: str) -> EasyDict:
if algorithm == 'PPOF':
cfg = PPOFPolicy.default_config()
if env_id == 'LunarLander-v2':
cfg.n_sample = 512
cfg.value_norm = 'popart'
cfg.entropy_weight = 1e-3
elif env_id == 'LunarLanderContinuous-v2':
cfg.action_space = 'continuous'
cfg.n_sample = 400
elif env_id == 'BipedalWalker-v3':
cfg.learning_rate = 1e-3
cfg.action_space = 'continuous'
cfg.n_sample = 1024
elif env_id == 'Pendulum-v1':
cfg.action_space = 'continuous'
cfg.n_sample = 400
elif env_id == 'acrobot':
cfg.learning_rate = 1e-4
cfg.n_sample = 400
elif env_id == 'rocket_landing':
cfg.n_sample = 2048
cfg.adv_norm = False
cfg.model = dict(
encoder_hidden_size_list=[64, 64, 128],
actor_head_hidden_size=128,
critic_head_hidden_size=128,
)
elif env_id == 'drone_fly':
cfg.action_space = 'continuous'
cfg.adv_norm = False
cfg.epoch_per_collect = 5
cfg.learning_rate = 5e-5
cfg.n_sample = 640
elif env_id == 'hybrid_moving':
cfg.action_space = 'hybrid'
cfg.n_sample = 3200
cfg.entropy_weight = 0.03
cfg.batch_size = 320
cfg.adv_norm = False
cfg.model = dict(
encoder_hidden_size_list=[256, 128, 64, 64],
sigma_type='fixed',
fixed_sigma_value=0.3,
bound_type='tanh',
)
elif env_id == 'evogym_carrier':
cfg.action_space = 'continuous'
cfg.n_sample = 2048
cfg.batch_size = 256
cfg.epoch_per_collect = 10
cfg.learning_rate = 3e-3
elif env_id == 'mario':
cfg.n_sample = 256
cfg.batch_size = 64
cfg.epoch_per_collect = 2
cfg.learning_rate = 1e-3
cfg.model = dict(
encoder_hidden_size_list=[64, 64, 128],
critic_head_hidden_size=128,
actor_head_hidden_size=128,
)
elif env_id == 'di_sheep':
cfg.n_sample = 3200
cfg.batch_size = 320
cfg.epoch_per_collect = 10
cfg.learning_rate = 3e-4
cfg.adv_norm = False
cfg.entropy_weight = 0.001
elif env_id == 'procgen_bigfish':
cfg.n_sample = 16384
cfg.batch_size = 16384
cfg.epoch_per_collect = 10
cfg.learning_rate = 5e-4
cfg.model = dict(
encoder_hidden_size_list=[64, 128, 256],
critic_head_hidden_size=256,
actor_head_hidden_size=256,
)
elif env_id in ['KangarooNoFrameskip-v4', 'BowlingNoFrameskip-v4']:
cfg.n_sample = 1024
cfg.batch_size = 128
cfg.epoch_per_collect = 10
cfg.learning_rate = 0.0001
cfg.model = dict(
encoder_hidden_size_list=[32, 64, 64, 128],
actor_head_hidden_size=128,
critic_head_hidden_size=128,
critic_head_layer_num=2,
)
elif env_id == 'PongNoFrameskip-v4':
cfg.n_sample = 3200
cfg.batch_size = 320
cfg.epoch_per_collect = 10
cfg.learning_rate = 3e-4
cfg.model = dict(
encoder_hidden_size_list=[64, 64, 128],
actor_head_hidden_size=128,
critic_head_hidden_size=128,
)
elif env_id == 'SpaceInvadersNoFrameskip-v4':
cfg.n_sample = 320
cfg.batch_size = 320
cfg.epoch_per_collect = 1
cfg.learning_rate = 1e-3
cfg.entropy_weight = 0.01
cfg.lr_scheduler = (2000, 0.1)
cfg.model = dict(
encoder_hidden_size_list=[64, 64, 128],
actor_head_hidden_size=128,
critic_head_hidden_size=128,
)
elif env_id == 'QbertNoFrameskip-v4':
cfg.n_sample = 3200
cfg.batch_size = 320
cfg.epoch_per_collect = 10
cfg.learning_rate = 5e-4
cfg.lr_scheduler = (1000, 0.1)
cfg.model = dict(
encoder_hidden_size_list=[64, 64, 128],
actor_head_hidden_size=128,
critic_head_hidden_size=128,
)
elif env_id == 'minigrid_fourroom':
cfg.n_sample = 3200
cfg.batch_size = 320
cfg.learning_rate = 3e-4
cfg.epoch_per_collect = 10
cfg.entropy_weight = 0.001
elif env_id == 'metadrive':
cfg.learning_rate = 3e-4
cfg.action_space = 'continuous'
cfg.entropy_weight = 0.001
cfg.n_sample = 3000
cfg.epoch_per_collect = 10
cfg.learning_rate = 0.0001
cfg.model = dict(
encoder_hidden_size_list=[32, 64, 64, 128],
actor_head_hidden_size=128,
critic_head_hidden_size=128,
critic_head_layer_num=2,
)
elif env_id == 'Hopper-v3':
cfg.action_space = "continuous"
cfg.n_sample = 3200
cfg.batch_size = 320
cfg.epoch_per_collect = 10
cfg.learning_rate = 3e-4
elif env_id == 'HalfCheetah-v3':
cfg.action_space = "continuous"
cfg.n_sample = 3200
cfg.batch_size = 320
cfg.epoch_per_collect = 10
cfg.learning_rate = 3e-4
elif env_id == 'Walker2d-v3':
cfg.action_space = "continuous"
cfg.n_sample = 3200
cfg.batch_size = 320
cfg.epoch_per_collect = 10
cfg.learning_rate = 3e-4
else:
raise KeyError("not supported env type: {}".format(env_id))
else:
raise KeyError("not supported algorithm type: {}".format(algorithm))
return cfg
def get_instance_env(env_id: str) -> BaseEnv:
if env_id == 'LunarLander-v2':
return DingEnvWrapper(gym.make('LunarLander-v2'))
elif env_id == 'LunarLanderContinuous-v2':
return DingEnvWrapper(gym.make('LunarLanderContinuous-v2', continuous=True))
elif env_id == 'BipedalWalker-v3':
return DingEnvWrapper(gym.make('BipedalWalker-v3'), cfg={'act_scale': True, 'rew_clip': True})
elif env_id == 'Pendulum-v1':
return DingEnvWrapper(gym.make('Pendulum-v1'), cfg={'act_scale': True})
elif env_id == 'acrobot':
return DingEnvWrapper(gym.make('Acrobot-v1'))
elif env_id == 'rocket_landing':
from dizoo.rocket.envs import RocketEnv
cfg = EasyDict({
'task': 'landing',
'max_steps': 800,
})
return RocketEnv(cfg)
elif env_id == 'drone_fly':
from dizoo.gym_pybullet_drones.envs import GymPybulletDronesEnv
cfg = EasyDict({
'env_id': 'flythrugate-aviary-v0',
'action_type': 'VEL',
})
return GymPybulletDronesEnv(cfg)
elif env_id == 'hybrid_moving':
import gym_hybrid
return DingEnvWrapper(gym.make('Moving-v0'))
elif env_id == 'evogym_carrier':
import evogym.envs
from evogym import sample_robot, WorldObject
path = os.path.join(os.path.dirname(__file__), '../../dizoo/evogym/envs/world_data/carry_bot.json')
robot_object = WorldObject.from_json(path)
body = robot_object.get_structure()
return DingEnvWrapper(
gym.make('Carrier-v0', body=body),
cfg={
'env_wrapper': [
lambda env: TimeLimitWrapper(env, max_limit=300),
lambda env: EvalEpisodeReturnWrapper(env),
]
}
)
elif env_id == 'mario':
import gym_super_mario_bros
from nes_py.wrappers import JoypadSpace
return DingEnvWrapper(
JoypadSpace(gym_super_mario_bros.make("SuperMarioBros-1-1-v1"), [["right"], ["right", "A"]]),
cfg={
'env_wrapper': [
lambda env: MaxAndSkipWrapper(env, skip=4),
lambda env: WarpFrameWrapper(env, size=84),
lambda env: ScaledFloatFrameWrapper(env),
lambda env: FrameStackWrapper(env, n_frames=4),
lambda env: TimeLimitWrapper(env, max_limit=200),
lambda env: EvalEpisodeReturnWrapper(env),
]
}
)
elif env_id == 'di_sheep':
from sheep_env import SheepEnv
return DingEnvWrapper(SheepEnv(level=9))
elif env_id == 'procgen_bigfish':
return DingEnvWrapper(
gym.make('procgen:procgen-bigfish-v0', start_level=0, num_levels=1),
cfg={
'env_wrapper': [
lambda env: TransposeWrapper(env),
lambda env: ScaledFloatFrameWrapper(env),
lambda env: EvalEpisodeReturnWrapper(env),
]
},
seed_api=False,
)
elif env_id == 'Hopper-v3':
cfg = EasyDict(
env_id='Hopper-v3',
env_wrapper='mujoco_default',
act_scale=True,
rew_clip=True,
)
return DingEnvWrapper(gym.make('Hopper-v3'), cfg=cfg)
elif env_id == 'HalfCheetah-v3':
cfg = EasyDict(
env_id='HalfCheetah-v3',
env_wrapper='mujoco_default',
act_scale=True,
rew_clip=True,
)
return DingEnvWrapper(gym.make('HalfCheetah-v3'), cfg=cfg)
elif env_id == 'Walker2d-v3':
cfg = EasyDict(
env_id='Walker2d-v3',
env_wrapper='mujoco_default',
act_scale=True,
rew_clip=True,
)
return DingEnvWrapper(gym.make('Walker2d-v3'), cfg=cfg)
elif env_id in [
'BowlingNoFrameskip-v4',
'BreakoutNoFrameskip-v4',
'GopherNoFrameskip-v4'
'KangarooNoFrameskip-v4',
'PongNoFrameskip-v4',
'QbertNoFrameskip-v4',
'SpaceInvadersNoFrameskip-v4',
]:
cfg = EasyDict({
'env_id': env_id,
'env_wrapper': 'atari_default',
})
ding_env_atari = DingEnvWrapper(gym.make(env_id), cfg=cfg)
return ding_env_atari
elif env_id == 'minigrid_fourroom':
import gymnasium
return DingEnvWrapper(
gymnasium.make('MiniGrid-FourRooms-v0'),
cfg={
'env_wrapper': [
lambda env: GymToGymnasiumWrapper(env),
lambda env: FlatObsWrapper(env),
lambda env: TimeLimitWrapper(env, max_limit=300),
lambda env: EvalEpisodeReturnWrapper(env),
]
}
)
elif env_id == 'metadrive':
from dizoo.metadrive.env.drive_env import MetaDrivePPOOriginEnv
from dizoo.metadrive.env.drive_wrapper import DriveEnvWrapper
cfg = dict(
map='XSOS',
horizon=4000,
out_of_road_penalty=40.0,
crash_vehicle_penalty=40.0,
out_of_route_done=True,
)
cfg = EasyDict(cfg)
return DriveEnvWrapper(MetaDrivePPOOriginEnv(cfg))
else:
raise KeyError("not supported env type: {}".format(env_id))
def get_hybrid_shape(action_space) -> EasyDict:
return EasyDict({
'action_type_shape': action_space[0].n,
'action_args_shape': action_space[1].shape,
})
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