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138ced23ba
* Currently this is specific to the case of discretizing a continuous action space. Can easily be adapted to other case by feeding the kNN otherwise, and removing the usage of a discretizing output action filter
58 lines
2.6 KiB
Python
58 lines
2.6 KiB
Python
from collections import OrderedDict
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from rl_coach.architectures.layers import Dense
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from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, EmbedderScheme
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from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps
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from rl_coach.environments.environment import SingleLevelSelection
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from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2
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from rl_coach.filters.action import BoxDiscretization
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from rl_coach.filters.filter import OutputFilter
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from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
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from rl_coach.graph_managers.graph_manager import ScheduleParameters
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from rl_coach.agents.wolpertinger_agent import WolpertingerAgentParameters
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####################
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# Graph Scheduling #
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####################
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schedule_params = ScheduleParameters()
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schedule_params.improve_steps = EnvironmentSteps(2000000)
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schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(20)
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schedule_params.evaluation_steps = EnvironmentEpisodes(1)
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schedule_params.heatup_steps = EnvironmentSteps(3000)
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#########
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# Agent #
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#########
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agent_params = WolpertingerAgentParameters()
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agent_params.network_wrappers['actor'].input_embedders_parameters['observation'].scheme = [Dense(400)]
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agent_params.network_wrappers['actor'].middleware_parameters.scheme = [Dense(300)]
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agent_params.network_wrappers['critic'].input_embedders_parameters['observation'].scheme = [Dense(400)]
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agent_params.network_wrappers['critic'].middleware_parameters.scheme = [Dense(300)]
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agent_params.network_wrappers['critic'].input_embedders_parameters['action'].scheme = EmbedderScheme.Empty
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agent_params.output_filter = \
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OutputFilter(
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action_filters=OrderedDict([
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('discretization', BoxDiscretization(num_bins_per_dimension=int(1e6)))
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]),
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is_a_reference_filter=False
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)
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###############
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# Environment #
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###############
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env_params = GymVectorEnvironment(level=SingleLevelSelection(mujoco_v2))
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########
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# Test #
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########
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preset_validation_params = PresetValidationParameters()
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preset_validation_params.test = True
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preset_validation_params.min_reward_threshold = 500
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preset_validation_params.max_episodes_to_achieve_reward = 1000
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preset_validation_params.reward_test_level = 'inverted_pendulum'
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preset_validation_params.trace_test_levels = ['inverted_pendulum']
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graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
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schedule_params=schedule_params, vis_params=VisualizationParameters(),
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preset_validation_params=preset_validation_params)
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