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61 lines
2.3 KiB
Python
61 lines
2.3 KiB
Python
import math
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from rl_coach.agents.ddqn_agent import DDQNAgentParameters
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from rl_coach.architectures.head_parameters import DuelingQHeadParameters
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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.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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from rl_coach.schedules import LinearSchedule
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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(1000)
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#########
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# Agent #
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#########
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agent_params = DDQNAgentParameters()
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# DDQN params
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agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(100)
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agent_params.algorithm.discount = 0.99
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agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(1)
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# NN configuration
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agent_params.network_wrappers['main'].learning_rate = 0.00025
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agent_params.network_wrappers['main'].replace_mse_with_huber_loss = False
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agent_params.network_wrappers['main'].heads_parameters = \
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[DuelingQHeadParameters(rescale_gradient_from_head_by_factor=1/math.sqrt(2))]
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# ER size
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agent_params.memory.max_size = (MemoryGranularity.Transitions, 40000)
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# E-Greedy schedule
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agent_params.exploration.epsilon_schedule = LinearSchedule(1.0, 0.01, 10000)
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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 = 250
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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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