Exemplos de ClippedPPOAgentParameters.pre_network_filter em Python

Exemplo n.º 1

Arquivo: CartPole_ClippedPPO.py Projeto: bigdatasciencegroup/intel-ailab-reinforcement-learning-coach

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

###############
# Environment #
###############
env_params = GymVectorEnvironment(level='CartPole-v0')
env_params.custom_reward_threshold = 200
# Set the target success
env_params.target_success_rate = 1.0


########
# Test #
########

Exemplo n.º 2

Arquivo: Mujoco_ClippedPPO.py Projeto: mdavala/coach

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.input_filter = MujocoInputFilter()
agent_params.exploration = AdditiveNoiseParameters()
agent_params.pre_network_filter = MujocoInputFilter()
agent_params.pre_network_filter.add_observation_filter(
    'observation', 'normalize_observation',
    ObservationNormalizationFilter(name='normalize_observation'))

###############
# Environment #
###############
env_params = Mujoco()
env_params.level = SingleLevelSelection(mujoco_v2)

vis_params = VisualizationParameters()
vis_params.video_dump_methods = [
    SelectedPhaseOnlyDumpMethod(RunPhase.TEST),
    MaxDumpMethod()
]