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f173e69187
* introduce dockerfiles. * ensure golden tests are run not just collected. * Skip CI download of dockerfiles. * add StarCraft environment and tests. * add minimaps starcraft validation parameters. * Add functional test running (from Ayoob) * pin mujoco_py version to a 1.5 compatible release. * fix config syntax issue. * pin remaining mujoco_py install calls. * Relax pin of gym version in gym Dockerfile. * update makefile based on functional test filtering.
70 lines
3.0 KiB
Python
70 lines
3.0 KiB
Python
from rl_coach.agents.actor_critic_agent import ActorCriticAgentParameters
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from rl_coach.agents.policy_optimization_agent import PolicyGradientRescaler
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from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
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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.starcraft2_environment import StarCraft2EnvironmentParameters
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from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
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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.schedules import ConstantSchedule
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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(50)
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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 = ActorCriticAgentParameters()
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agent_params.algorithm.policy_gradient_rescaler = PolicyGradientRescaler.GAE
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agent_params.algorithm.apply_gradients_every_x_episodes = 1
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agent_params.algorithm.num_steps_between_gradient_updates = 20
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agent_params.algorithm.gae_lambda = 0.96
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agent_params.algorithm.beta_entropy = 0
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agent_params.network_wrappers['main'].clip_gradients = 10.0
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agent_params.network_wrappers['main'].learning_rate = 0.00001
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# agent_params.network_wrappers['main'].batch_size = 20
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agent_params.network_wrappers['main'].input_embedders_parameters = {
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"screen": InputEmbedderParameters(input_rescaling={'image': 3.0})
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}
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agent_params.exploration = AdditiveNoiseParameters()
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agent_params.exploration.noise_percentage_schedule = ConstantSchedule(0.05)
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# agent_params.exploration.noise_percentage_schedule = LinearSchedule(0.4, 0.05, 100000)
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agent_params.exploration.evaluation_noise_percentage = 0.05
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agent_params.network_wrappers['main'].batch_size = 64
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agent_params.network_wrappers['main'].optimizer_epsilon = 1e-5
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agent_params.network_wrappers['main'].adam_optimizer_beta2 = 0.999
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###############
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# Environment #
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###############
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env_params = StarCraft2EnvironmentParameters(level='CollectMineralShards')
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env_params.feature_screen_maps_to_use = [5]
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env_params.feature_minimap_maps_to_use = [5]
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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 = 50
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preset_validation_params.max_episodes_to_achieve_reward = 200
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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,
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vis_params=VisualizationParameters(),
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preset_validation_params=preset_validation_params)
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