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43 lines
1.9 KiB
Python
43 lines
1.9 KiB
Python
from rl_coach.agents.ddqn_agent import DDQNAgentParameters
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from rl_coach.architectures.tensorflow_components.heads.dueling_q_head import DuelingQHeadParameters
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from rl_coach.base_parameters import VisualizationParameters
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from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
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from rl_coach.environments.doom_environment import DoomEnvironmentParameters
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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(50)
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schedule_params.evaluation_steps = EnvironmentEpisodes(3)
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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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agent_params.memory.max_size = (MemoryGranularity.Transitions, 5000)
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agent_params.network_wrappers['main'].learning_rate = 0.00025
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agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(1000)
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agent_params.exploration.epsilon_schedule = LinearSchedule(0.5, 0.01, 50000)
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agent_params.exploration.evaluation_epsilon = 0
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agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(1)
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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 = [DuelingQHeadParameters()]
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###############
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# Environment #
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###############
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env_params = DoomEnvironmentParameters(level='basic')
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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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