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rainbow dqn hyper-parameter updates
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@@ -22,7 +22,8 @@ from rl_coach.agents.categorical_dqn_agent import CategoricalDQNAlgorithmParamet
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CategoricalDQNAgent, CategoricalDQNAgentParameters
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CategoricalDQNAgent, CategoricalDQNAgentParameters
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from rl_coach.agents.dqn_agent import DQNNetworkParameters
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from rl_coach.agents.dqn_agent import DQNNetworkParameters
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from rl_coach.architectures.tensorflow_components.heads.rainbow_q_head import RainbowQHeadParameters
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from rl_coach.architectures.tensorflow_components.heads.rainbow_q_head import RainbowQHeadParameters
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from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
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from rl_coach.base_parameters import MiddlewareScheme
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from rl_coach.exploration_policies.parameter_noise import ParameterNoiseParameters
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from rl_coach.exploration_policies.parameter_noise import ParameterNoiseParameters
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from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplayParameters, \
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from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplayParameters, \
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PrioritizedExperienceReplay
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PrioritizedExperienceReplay
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@@ -32,6 +33,7 @@ class RainbowDQNNetworkParameters(DQNNetworkParameters):
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def __init__(self):
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def __init__(self):
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super().__init__()
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super().__init__()
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self.heads_parameters = [RainbowQHeadParameters()]
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self.heads_parameters = [RainbowQHeadParameters()]
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self.middleware_parameters = FCMiddlewareParameters(scheme=MiddlewareScheme.Empty)
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class RainbowDQNAlgorithmParameters(CategoricalDQNAlgorithmParameters):
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class RainbowDQNAlgorithmParameters(CategoricalDQNAlgorithmParameters):
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@@ -44,6 +46,11 @@ class RainbowDQNExplorationParameters(ParameterNoiseParameters):
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super().__init__(agent_params)
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super().__init__(agent_params)
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class RainbowDQNMemoryParameters(PrioritizedExperienceReplayParameters):
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def __init__(self):
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super().__init__()
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class RainbowDQNAgentParameters(CategoricalDQNAgentParameters):
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class RainbowDQNAgentParameters(CategoricalDQNAgentParameters):
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def __init__(self):
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def __init__(self):
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super().__init__()
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super().__init__()
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@@ -58,8 +65,8 @@ class RainbowDQNAgentParameters(CategoricalDQNAgentParameters):
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# Rainbow Deep Q Network - https://arxiv.org/abs/1710.02298
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# Rainbow Deep Q Network - https://arxiv.org/abs/1710.02298
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# Agent implementation is WIP. Currently has:
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# Agent implementation is WIP. Currently is composed of:
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# 1. DQN
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# 1. NoisyNets
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# 2. C51
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# 2. C51
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# 3. Prioritized ER
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# 3. Prioritized ER
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# 4. DDQN
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# 4. DDQN
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@@ -1,4 +1,3 @@
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from rl_coach.agents.categorical_dqn_agent import CategoricalDQNAgentParameters
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from rl_coach.agents.rainbow_dqn_agent import RainbowDQNAgentParameters
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from rl_coach.agents.rainbow_dqn_agent import RainbowDQNAgentParameters
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from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters
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from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters
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from rl_coach.core_types import EnvironmentSteps, RunPhase
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from rl_coach.core_types import EnvironmentSteps, RunPhase
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@@ -13,17 +12,20 @@ from rl_coach.schedules import LinearSchedule
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####################
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####################
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schedule_params = ScheduleParameters()
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schedule_params = ScheduleParameters()
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schedule_params.improve_steps = EnvironmentSteps(50000000)
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schedule_params.improve_steps = EnvironmentSteps(50000000)
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schedule_params.steps_between_evaluation_periods = EnvironmentSteps(250000)
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schedule_params.steps_between_evaluation_periods = EnvironmentSteps(1000000)
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schedule_params.evaluation_steps = EnvironmentSteps(135000)
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schedule_params.evaluation_steps = EnvironmentSteps(125000)
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schedule_params.heatup_steps = EnvironmentSteps(50000)
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schedule_params.heatup_steps = EnvironmentSteps(20000)
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#########
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#########
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# Agent #
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# Agent #
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#########
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#########
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agent_params = RainbowDQNAgentParameters()
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agent_params = RainbowDQNAgentParameters()
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agent_params.network_wrappers['main'].learning_rate = 0.00025
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agent_params.memory.beta = LinearSchedule(0.4, 1, 12500000) # 12.5M training iterations = 50M steps = 200M frames
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agent_params.network_wrappers['main'].learning_rate = 0.0000625
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agent_params.network_wrappers['main'].optimizer_epsilon = 1.5e-4
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agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(32000 // 4) # 32k frames
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agent_params.memory.beta = LinearSchedule(0.4, 1, 12500000) # 12.5M training iterations = 50M steps = 200M frames
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agent_params.memory.alpha = 0.5
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###############
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###############
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# Environment #
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# Environment #
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