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58 lines
2.8 KiB
Python
58 lines
2.8 KiB
Python
from rl_coach.agents.dfp_agent import DFPAgentParameters
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from rl_coach.base_parameters import VisualizationParameters
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from rl_coach.environments.environment import MaxDumpMethod, SelectedPhaseOnlyDumpMethod
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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 LinearSchedule
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from rl_coach.core_types import TrainingSteps, EnvironmentSteps, RunPhase
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from rl_coach.environments.doom_environment import DoomEnvironmentParameters, DoomEnvironment
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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 = EnvironmentSteps(6250000)
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schedule_params.steps_between_evaluation_periods = EnvironmentSteps(62500)
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schedule_params.evaluation_steps = EnvironmentSteps(6250)
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schedule_params.heatup_steps = EnvironmentSteps(1)
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#########
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# Agent #
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#########
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agent_params = DFPAgentParameters()
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agent_params.network_wrappers['main'].learning_rate = 0.0001
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# the original DFP code decays epsilon in ~1.5M steps. Only that unlike other most other papers, these are 1.5M
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# training steps. i.e. it is equivalent to once every 8 playing steps (when a training batch is sampled).
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# so this is 1.5M*8 =~ 12M playing steps per worker.
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# TODO allow the epsilon schedule to be defined in terms of training steps.
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agent_params.exploration.epsilon_schedule = LinearSchedule(1, 0, 12000000)
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agent_params.exploration.evaluation_epsilon = 0
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agent_params.algorithm.use_accumulated_reward_as_measurement = False
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agent_params.algorithm.goal_vector = [0.5, 0.5, 1] # ammo, health, frag count
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agent_params.network_wrappers['main'].input_embedders_parameters['measurements'].input_rescaling['vector'] = 100.
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agent_params.algorithm.scale_measurements_targets['GameVariable.HEALTH'] = 30.0
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agent_params.algorithm.scale_measurements_targets['GameVariable.AMMO2'] = 7.5
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agent_params.algorithm.scale_measurements_targets['GameVariable.USER2'] = 1.0
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agent_params.network_wrappers['main'].learning_rate_decay_rate = 0.3
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agent_params.network_wrappers['main'].learning_rate_decay_steps = 250000
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agent_params.network_wrappers['main'].input_embedders_parameters['measurements'].input_offset['vector'] = 0.5
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agent_params.network_wrappers['main'].input_embedders_parameters['observation'].input_offset['vector'] = 0.5
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###############
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# Environment #
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###############
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env_params = DoomEnvironmentParameters()
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env_params.level = 'BATTLE_COACH_LOCAL'
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env_params.cameras = [DoomEnvironment.CameraTypes.OBSERVATION]
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vis_params = VisualizationParameters()
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vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()]
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vis_params.dump_mp4 = False
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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=vis_params)
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