Python GymVectorEnvironment.additional_simulator_parameters 예제들

예제 #1

파일: BitFlip_DQN.py 프로젝트: wwxFromTju/coach

agent_params.algorithm.num_consecutive_playing_steps = EnvironmentEpisodes(16)
agent_params.algorithm.num_consecutive_training_steps = 40
agent_params.algorithm.num_steps_between_copying_online_weights_to_target = TrainingSteps(
    40)
agent_params.algorithm.rate_for_copying_weights_to_target = 0.05
agent_params.memory.max_size = (MemoryGranularity.Transitions, 10**6)
agent_params.exploration.epsilon_schedule = ConstantSchedule(0.2)
agent_params.exploration.evaluation_epsilon = 0

###############
# Environment #
###############
env_params = GymVectorEnvironment(
    level='rl_coach.environments.toy_problems.bit_flip:BitFlip')
env_params.additional_simulator_parameters = {
    'bit_length': bit_length,
    'mean_zero': True
}
env_params.custom_reward_threshold = -bit_length + 1

########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
preset_validation_params.min_reward_threshold = -7.9
preset_validation_params.max_episodes_to_achieve_reward = 10000

graph_manager = BasicRLGraphManager(
    agent_params=agent_params,
    env_params=env_params,
    schedule_params=schedule_params,

예제 #2

파일: preset-energy-plus-clipped-ppo.py 프로젝트: FNDaily/amazon-sagemaker-examples

agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoachSynchronizationType.SYNC

agent_params.exploration = EGreedyParameters()
agent_params.exploration.epsilon_schedule = LinearSchedule(1.0, 0.01, 10000)
agent_params.pre_network_filter.add_observation_filter('observation', 'normalize_observation',
    ObservationNormalizationFilter(name='normalize_observation'))

###############
# Environment #
###############
config = {
    'eplus_path': '/usr/local/EnergyPlus-8-8-0/',
    'weather_file': 'weather/USA_CA_San.Francisco.Intl.AP.724940_TMY3.epw'
}
env_params = GymVectorEnvironment(level='eplus.envs.data_center_env:DataCenterEnv')
env_params.additional_simulator_parameters = {'config': config }

#################
# Visualization #
#################

vis_params = VisualizationParameters()
vis_params.dump_gifs = False

########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
preset_validation_params.min_reward_threshold = 150
preset_validation_params.max_episodes_to_achieve_reward = 400

예제 #3

파일: preset-energy-plus-clipped-ppo.py 프로젝트: dhawalkp/amazon-sagemaker-examples

agent_params.pre_network_filter.add_observation_filter(
    "observation",
    "normalize_observation",
    ObservationNormalizationFilter(name="normalize_observation"),
)

###############
# Environment #
###############
config = {
    "eplus_path": "/usr/local/EnergyPlus-8-8-0/",
    "weather_file": "weather/USA_CA_San.Francisco.Intl.AP.724940_TMY3.epw",
}
env_params = GymVectorEnvironment(
    level="eplus.envs.data_center_env:DataCenterEnv")
env_params.additional_simulator_parameters = {"config": config}

#################
# Visualization #
#################

vis_params = VisualizationParameters()
vis_params.dump_gifs = False

########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
preset_validation_params.min_reward_threshold = 150
preset_validation_params.max_episodes_to_achieve_reward = 400

예제 #4

agent_params = BootstrappedDQNAgentParameters()
agent_params.network_wrappers['main'].learning_rate = 0.00025
agent_params.memory.max_size = (MemoryGranularity.Transitions, 1000000)
agent_params.algorithm.discount = 0.99
agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(4)
agent_params.network_wrappers['main'].heads_parameters[
    0].num_output_head_copies = num_output_head_copies
agent_params.network_wrappers['main'].heads_parameters[
    0].rescale_gradient_from_head_by_factor = 1.0 / num_output_head_copies
agent_params.exploration.bootstrapped_data_sharing_probability = 1.0
agent_params.exploration.architecture_num_q_heads = num_output_head_copies
agent_params.exploration.epsilon_schedule = ConstantSchedule(0)
agent_params.input_filter = NoInputFilter()
agent_params.output_filter = NoOutputFilter()

###############
# Environment #
###############
env_params = GymVectorEnvironment(
    level=
    'rl_coach.environments.toy_problems.exploration_chain:ExplorationChain')
env_params.additional_simulator_parameters = {
    'chain_length': N,
    'max_steps': N + 7
}

graph_manager = BasicRLGraphManager(agent_params=agent_params,
                                    env_params=env_params,
                                    schedule_params=schedule_params,
                                    vis_params=VisualizationParameters())

예제 #5

# Agent params
agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(
    100)
agent_params.algorithm.discount = 0.99
agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(4096)
agent_params.algorithm.act_for_full_episodes = False

# NN configuration
agent_params.network_wrappers['main'].input_embedders_parameters = {
    'observation': InputEmbedderParameters(scheme=[])
}
agent_params.network_wrappers['main'].learning_rate = 0.001

################
#  Environment #
################
env_params = GymVectorEnvironment(
    level='gym_jiminy.envs.acrobot:JiminyAcrobotEnv')
env_params.additional_simulator_parameters = {
    'continuous': True,
    'enableGoalEnv': False
}

################
#   Learning   #
################
graph_manager = BasicRLGraphManager(agent_params=agent_params,
                                    env_params=env_params,
                                    schedule_params=SimpleSchedule())
graph_manager.improve()

예제 #6

agent_params.exploration = EGreedyParameters()
agent_params.exploration.epsilon_schedule = LinearSchedule(1.0, 0.01, 10000)
agent_params.pre_network_filter.add_observation_filter(
    'observation', 'normalize_observation',
    ObservationNormalizationFilter(name='normalize_observation'))

###############
# Environment #
###############
config = {
    'eplus_path': '/usr/local/EnergyPlus-8-8-0/',
    'weather_file': 'weather/USA_CA_San.Francisco.Intl.AP.724940_TMY3.epw'
}
env_params = GymVectorEnvironment(
    level='eplus.envs.data_center_env:DataCenterEnv')
env_params.additional_simulator_parameters = {'config': config}

#################
# Visualization #
#################

vis_params = VisualizationParameters()
vis_params.dump_gifs = False

########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
preset_validation_params.min_reward_threshold = 150
preset_validation_params.max_episodes_to_achieve_reward = 400

예제 #7

파일: Pendulum_HAC.py 프로젝트: wwxFromTju/coach

bottom_critic.middleware_parameters.scheme = [Dense(64)] * 3
bottom_critic.learning_rate = 0.001
bottom_critic.batch_size = 4096

agents_params = [top_agent_params, bottom_agent_params]

###############
# Environment #
###############
time_limit = 1000

env_params = GymVectorEnvironment(
    level="rl_coach.environments.mujoco.pendulum_with_goals:PendulumWithGoals")
env_params.additional_simulator_parameters = {
    "time_limit": time_limit,
    "random_goals_instead_of_standing_goal": False,
    "polar_coordinates": polar_coordinates,
    "goal_reaching_thresholds": distance_from_goal_threshold
}
env_params.frame_skip = 10
env_params.custom_reward_threshold = -time_limit + 1

vis_params = VisualizationParameters()
vis_params.native_rendering = False

graph_manager = HACGraphManager(
    agents_params=agents_params,
    env_params=env_params,
    schedule_params=schedule_params,
    vis_params=vis_params,
    consecutive_steps_to_run_non_top_levels=EnvironmentSteps(40))