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2b5d1dabe6
* initial ACER commit * Code cleanup + several fixes * Q-retrace bug fix + small clean-ups * added documentation for acer * ACER benchmarks * update benchmarks table * Add nightly running of golden and trace tests. (#202) Resolves #200 * comment out nightly trace tests until values reset. * remove redundant observe ignore (#168) * ensure nightly test env containers exist. (#205) Also bump integration test timeout * wxPython removal (#207) Replacing wxPython with Python's Tkinter. Also removing the option to choose multiple files as it is unused and causes errors, and fixing the load file/directory spinner. * Create CONTRIBUTING.md (#210) * Create CONTRIBUTING.md. Resolves #188 * run nightly golden tests sequentially. (#217) Should reduce resource requirements and potential CPU contention but increases overall execution time. * tests: added new setup configuration + test args (#211) - added utils for future tests and conftest - added test args * new docs build * golden test update
50 lines
2.1 KiB
Python
50 lines
2.1 KiB
Python
from rl_coach.agents.acer_agent import ACERAgentParameters
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from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters
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from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
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from rl_coach.environments.gym_environment import GymVectorEnvironment
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from rl_coach.filters.filter import InputFilter
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from rl_coach.filters.reward.reward_rescale_filter import RewardRescaleFilter
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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.memories.memory import MemoryGranularity
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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 = TrainingSteps(10000000000)
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schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(10)
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schedule_params.evaluation_steps = EnvironmentEpisodes(1)
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schedule_params.heatup_steps = EnvironmentSteps(0)
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#########
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# Agent #
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#########
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agent_params = ACERAgentParameters()
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agent_params.algorithm.num_steps_between_gradient_updates = 5
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agent_params.algorithm.ratio_of_replay = 4
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agent_params.algorithm.num_transitions_to_start_replay = 1000
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agent_params.memory.max_size = (MemoryGranularity.Transitions, 50000)
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agent_params.input_filter = InputFilter()
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agent_params.input_filter.add_reward_filter('rescale', RewardRescaleFilter(1/200.))
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agent_params.algorithm.beta_entropy = 0.0
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###############
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# Environment #
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###############
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env_params = GymVectorEnvironment(level='CartPole-v0')
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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 = 150
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preset_validation_params.max_episodes_to_achieve_reward = 300
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preset_validation_params.num_workers = 1
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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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