src/markov/presets/left_stereo_deeper.py [68:124]:
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        }

agent_params.network_wrappers['main'].learning_rate = 0.0003
agent_params.network_wrappers['main'].middleware_parameters.activation_function = 'relu'
agent_params.network_wrappers['main'].batch_size = 64
agent_params.network_wrappers['main'].optimizer_epsilon = 1e-5
agent_params.network_wrappers['main'].adam_optimizer_beta2 = 0.999

# agent_params.network_wrappers['main'].learning_rate_decay_steps = 60000
# agent_params.network_wrappers['main'].learning_rate_decay_rate = 0.95
# agent_params.network_wrappers['main'].input_embedders_parameters['observation'].batchnorm = True
# agent_params.network_wrappers['main'].input_embedders_parameters['observation'].dropout_rate = 0.3
# agent_params.network_wrappers['main'].l2_regularization = 2e-5
agent_params.algorithm.beta_entropy = 0.001

agent_params.algorithm.clip_likelihood_ratio_using_epsilon = 0.2
agent_params.algorithm.clipping_decay_schedule = LinearSchedule(1.0, 0, 1000000)
agent_params.algorithm.gae_lambda = 0.95
agent_params.algorithm.discount = 0.999
agent_params.algorithm.optimization_epochs = 5
agent_params.algorithm.estimate_state_value_using_gae = True
agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentEpisodes(20)
agent_params.algorithm.num_consecutive_playing_steps = EnvironmentEpisodes(20)

agent_params.exploration = CategoricalParameters()

agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoachSynchronizationType.SYNC


###############
# Environment #
###############
SilverstoneInputFilter = InputFilter(is_a_reference_filter=True)

SilverstoneInputFilter.add_observation_filter('left_camera', 'to_grayscale', ObservationRGBToYFilter())
SilverstoneInputFilter.add_observation_filter('left_camera', 'to_uint8', ObservationToUInt8Filter(0, 255))
SilverstoneInputFilter.add_observation_filter('left_camera', 'stacking', ObservationStackingFilter(1))
SilverstoneInputFilter.add_observation_filter('stereo', 'to_uint8', ObservationToUInt8Filter(0, 255))

env_params = GymVectorEnvironment()
env_params.default_input_filter = SilverstoneInputFilter
env_params.level = 'DeepRacerRacetrackCustomActionSpaceEnv-v0'

vis_params = VisualizationParameters()
vis_params.dump_mp4 = False

########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
preset_validation_params.min_reward_threshold = 400
preset_validation_params.max_episodes_to_achieve_reward = 1000

graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
                                    schedule_params=schedule_params, vis_params=vis_params,
                                    preset_validation_params=preset_validation_params)
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src/markov/presets/only_stereo.py [42:98]:
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        }

agent_params.network_wrappers['main'].learning_rate = 0.0003
agent_params.network_wrappers['main'].middleware_parameters.activation_function = 'relu'
agent_params.network_wrappers['main'].batch_size = 64
agent_params.network_wrappers['main'].optimizer_epsilon = 1e-5
agent_params.network_wrappers['main'].adam_optimizer_beta2 = 0.999

# agent_params.network_wrappers['main'].learning_rate_decay_steps = 60000
# agent_params.network_wrappers['main'].learning_rate_decay_rate = 0.95
# agent_params.network_wrappers['main'].input_embedders_parameters['observation'].batchnorm = True
# agent_params.network_wrappers['main'].input_embedders_parameters['observation'].dropout_rate = 0.3
# agent_params.network_wrappers['main'].l2_regularization = 2e-5
agent_params.algorithm.beta_entropy = 0.001

agent_params.algorithm.clip_likelihood_ratio_using_epsilon = 0.2
agent_params.algorithm.clipping_decay_schedule = LinearSchedule(1.0, 0, 1000000)
agent_params.algorithm.gae_lambda = 0.95
agent_params.algorithm.discount = 0.999
agent_params.algorithm.optimization_epochs = 5
agent_params.algorithm.estimate_state_value_using_gae = True
agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentEpisodes(20)
agent_params.algorithm.num_consecutive_playing_steps = EnvironmentEpisodes(20)

agent_params.exploration = CategoricalParameters()

agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoachSynchronizationType.SYNC


###############
# Environment #
###############
SilverstoneInputFilter = InputFilter(is_a_reference_filter=True)

SilverstoneInputFilter.add_observation_filter('left_camera', 'to_grayscale', ObservationRGBToYFilter())
SilverstoneInputFilter.add_observation_filter('left_camera', 'to_uint8', ObservationToUInt8Filter(0, 255))
SilverstoneInputFilter.add_observation_filter('left_camera', 'stacking', ObservationStackingFilter(1))
SilverstoneInputFilter.add_observation_filter('stereo', 'to_uint8', ObservationToUInt8Filter(0, 255))

env_params = GymVectorEnvironment()
env_params.default_input_filter = SilverstoneInputFilter
env_params.level = 'DeepRacerRacetrackCustomActionSpaceEnv-v0'

vis_params = VisualizationParameters()
vis_params.dump_mp4 = False

########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
preset_validation_params.min_reward_threshold = 400
preset_validation_params.max_episodes_to_achieve_reward = 1000

graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
                                    schedule_params=schedule_params, vis_params=vis_params,
                                    preset_validation_params=preset_validation_params)
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