mirror of
https://github.com/gryf/coach.git
synced 2025-12-18 11:40:18 +01:00
71 lines
3.4 KiB
Python
71 lines
3.4 KiB
Python
from rl_coach.agents.clipped_ppo_agent import ClippedPPOAgentParameters
|
|
from rl_coach.architectures.layers import Dense
|
|
from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, DistributedCoachSynchronizationType
|
|
from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, RunPhase
|
|
from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2
|
|
from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
|
|
from rl_coach.exploration_policies.e_greedy import EGreedyParameters
|
|
from rl_coach.filters.observation.observation_normalization_filter import ObservationNormalizationFilter
|
|
from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
|
|
from rl_coach.graph_managers.graph_manager import ScheduleParameters
|
|
from rl_coach.schedules import LinearSchedule
|
|
|
|
####################
|
|
# Graph Scheduling #
|
|
####################
|
|
|
|
schedule_params = ScheduleParameters()
|
|
schedule_params.improve_steps = TrainingSteps(10000000)
|
|
schedule_params.steps_between_evaluation_periods = EnvironmentSteps(2048)
|
|
schedule_params.evaluation_steps = EnvironmentEpisodes(5)
|
|
schedule_params.heatup_steps = EnvironmentSteps(0)
|
|
|
|
#########
|
|
# Agent #
|
|
#########
|
|
agent_params = ClippedPPOAgentParameters()
|
|
|
|
|
|
agent_params.network_wrappers['main'].learning_rate = 0.0003
|
|
agent_params.network_wrappers['main'].input_embedders_parameters['observation'].activation_function = 'tanh'
|
|
agent_params.network_wrappers['main'].input_embedders_parameters['observation'].scheme = [Dense(64)]
|
|
agent_params.network_wrappers['main'].middleware_parameters.scheme = [Dense(64)]
|
|
agent_params.network_wrappers['main'].middleware_parameters.activation_function = 'tanh'
|
|
agent_params.network_wrappers['main'].batch_size = 64
|
|
agent_params.network_wrappers['main'].optimizer_epsilon = 1e-5
|
|
agent_params.network_wrappers['main'].adam_optimizer_beta2 = 0.999
|
|
|
|
agent_params.algorithm.clip_likelihood_ratio_using_epsilon = 0.2
|
|
agent_params.algorithm.clipping_decay_schedule = LinearSchedule(1.0, 0, 1000000)
|
|
agent_params.algorithm.beta_entropy = 0
|
|
agent_params.algorithm.gae_lambda = 0.95
|
|
agent_params.algorithm.discount = 0.99
|
|
agent_params.algorithm.optimization_epochs = 10
|
|
agent_params.algorithm.estimate_state_value_using_gae = True
|
|
agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(2048)
|
|
|
|
# Distributed Coach synchronization type.
|
|
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 #
|
|
###############
|
|
env_params = GymVectorEnvironment(level='CartPole-v0')
|
|
|
|
########
|
|
# 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
|
|
|
|
graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
|
|
schedule_params=schedule_params, vis_params=VisualizationParameters(),
|
|
preset_validation_params=preset_validation_params)
|