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pre-release 0.10.0

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Gal Novik
2018-08-13 17:11:34 +03:00
parent d44c329bb8
commit 19ca5c24b1
485 changed files with 33292 additions and 16770 deletions
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rl_coach/presets/ControlSuite_DDPG.py Normal file
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from rl_coach.agents.ddpg_agent import DDPGAgentParameters
from rl_coach.architectures.tensorflow_components.architecture import Dense
from rl_coach.base_parameters import VisualizationParameters, EmbedderScheme, PresetValidationParameters
from rl_coach.environments.control_suite_environment import ControlSuiteEnvironmentParameters, control_suite_envs
from rl_coach.environments.environment import MaxDumpMethod, SelectedPhaseOnlyDumpMethod, SingleLevelSelection
from rl_coach.environments.gym_environment import MujocoInputFilter
from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
from rl_coach.graph_managers.graph_manager import ScheduleParameters
from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, RunPhase
from rl_coach.filters.reward.reward_rescale_filter import RewardRescaleFilter
####################
# Graph Scheduling #
####################
schedule_params = ScheduleParameters()
schedule_params.improve_steps = TrainingSteps(10000000000)
schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(20)
schedule_params.evaluation_steps = EnvironmentEpisodes(1)
schedule_params.heatup_steps = EnvironmentSteps(1000)
#########
# Agent #
#########
agent_params = DDPGAgentParameters()
agent_params.network_wrappers['actor'].input_embedders_parameters['measurements'] = \
agent_params.network_wrappers['actor'].input_embedders_parameters.pop('observation')
agent_params.network_wrappers['critic'].input_embedders_parameters['measurements'] = \
agent_params.network_wrappers['critic'].input_embedders_parameters.pop('observation')
agent_params.network_wrappers['actor'].input_embedders_parameters['measurements'].scheme = [Dense([300])]
agent_params.network_wrappers['actor'].middleware_parameters.scheme = [Dense([200])]
agent_params.network_wrappers['critic'].input_embedders_parameters['measurements'].scheme = [Dense([400])]
agent_params.network_wrappers['critic'].middleware_parameters.scheme = [Dense([300])]
agent_params.network_wrappers['critic'].input_embedders_parameters['action'].scheme = EmbedderScheme.Empty
agent_params.input_filter = MujocoInputFilter()
agent_params.input_filter.add_reward_filter("rescale", RewardRescaleFilter(1/10.))
###############
# Environment #
###############
env_params = ControlSuiteEnvironmentParameters()
env_params.level = SingleLevelSelection(control_suite_envs)
vis_params = VisualizationParameters()
vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()]
vis_params.dump_mp4 = False
########
# Test #
########
# this preset is too slow to test on a regular basis
# preset_validation_params = PresetValidationParameters()
# preset_validation_params.test = True
# preset_validation_params.min_reward_threshold = 150
# preset_validation_params.max_episodes_to_achieve_reward = 250
graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
schedule_params=schedule_params, vis_params=vis_params,)
# preset_validation_params=preset_validation_params)