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51 lines
2.4 KiB
Python
51 lines
2.4 KiB
Python
from rl_coach.agents.bootstrapped_dqn_agent import BootstrappedDQNAgentParameters
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from rl_coach.base_parameters import VisualizationParameters
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from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps
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from rl_coach.environments.gym_environment import GymVectorEnvironment
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from rl_coach.exploration_policies.ucb import UCBParameters
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from rl_coach.filters.filter import NoInputFilter, NoOutputFilter
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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 ConstantSchedule
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N = 20
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num_output_head_copies = 20
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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 = EnvironmentEpisodes(2000)
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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(N)
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####################
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# DQN Agent Params #
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####################
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agent_params = BootstrappedDQNAgentParameters()
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agent_params.network_wrappers['main'].learning_rate = 0.00025
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agent_params.memory.max_size = (MemoryGranularity.Transitions, 1000000)
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agent_params.algorithm.discount = 0.99
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agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(4)
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agent_params.network_wrappers['main'].heads_parameters[0].num_output_head_copies = num_output_head_copies
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agent_params.network_wrappers['main'].heads_parameters[0].rescale_gradient_from_head_by_factor = 1.0/num_output_head_copies
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agent_params.exploration = UCBParameters()
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agent_params.exploration.bootstrapped_data_sharing_probability = 1.0
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agent_params.exploration.architecture_num_q_heads = num_output_head_copies
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agent_params.exploration.epsilon_schedule = ConstantSchedule(0)
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agent_params.exploration.lamb = 10
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agent_params.input_filter = NoInputFilter()
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agent_params.output_filter = NoOutputFilter()
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###############
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# Environment #
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###############
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env_params = GymVectorEnvironment(level='rl_coach.environments.toy_problems.exploration_chain:ExplorationChain')
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env_params.additional_simulator_parameters = {'chain_length': N, 'max_steps': N+7}
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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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