Exemplos de BootstrappedDQNAgentParameters em Python

Exemplo n.º 1

num_output_head_copies = 20

####################
# Graph Scheduling #
####################

schedule_params = ScheduleParameters()
schedule_params.improve_steps = EnvironmentEpisodes(2000)
schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(10)
schedule_params.evaluation_steps = EnvironmentEpisodes(1)
schedule_params.heatup_steps = EnvironmentSteps(N)

####################
# DQN Agent Params #
####################
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()

###############

Exemplo n.º 2

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

from rl_coach.agents.bootstrapped_dqn_agent import BootstrappedDQNAgentParameters
from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters
from rl_coach.environments.environment import SingleLevelSelection
from rl_coach.environments.gym_environment import Atari, atari_deterministic_v4, atari_schedule
from rl_coach.exploration_policies.ucb import UCBParameters
from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager

#########
# Agent #
#########
agent_params = BootstrappedDQNAgentParameters()
agent_params.network_wrappers['main'].learning_rate = 0.00025
agent_params.exploration = UCBParameters()

###############
# Environment #
###############
env_params = Atari(level=SingleLevelSelection(atari_deterministic_v4))

########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.trace_test_levels = ['breakout', 'pong', 'space_invaders']

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

Exemplo n.º 3

Arquivo: Atari_Bootstrapped_DQN.py Projeto: mdavala/coach

from rl_coach.graph_managers.graph_manager import ScheduleParameters

####################
# Graph Scheduling #
####################

schedule_params = ScheduleParameters()
schedule_params.improve_steps = EnvironmentSteps(50000000)
schedule_params.steps_between_evaluation_periods = EnvironmentSteps(250000)
schedule_params.evaluation_steps = EnvironmentSteps(135000)
schedule_params.heatup_steps = EnvironmentSteps(50000)

#########
# Agent #
#########
agent_params = BootstrappedDQNAgentParameters()
agent_params.network_wrappers['main'].learning_rate = 0.00025

###############
# Environment #
###############
env_params = Atari()
env_params.level = SingleLevelSelection(atari_deterministic_v4)

vis_params = VisualizationParameters()
vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()]
vis_params.dump_mp4 = False

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