src/markov/presets/only_lidar.py [79:106]:
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agent_params.network_wrappers['main'].batch_size = 128
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 = 50000
agent_params.network_wrappers['main'].learning_rate_decay_rate = 0.95

agent_params.algorithm.clip_likelihood_ratio_using_epsilon = 0.2
agent_params.algorithm.clipping_decay_schedule = LinearSchedule(1.0, 0, 1000000)
agent_params.algorithm.beta_entropy = 0.01  # also try 0.001
agent_params.algorithm.gae_lambda = 0.95
agent_params.algorithm.discount = 0.999
agent_params.algorithm.optimization_epochs = 10
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('front_camera', 'to_grayscale', ObservationRGBToYFilter())
SilverstoneInputFilter.add_observation_filter('front_camera', 'to_uint8', ObservationToUInt8Filter(0, 255))
SilverstoneInputFilter.add_observation_filter('front_camera', 'stacking', ObservationStackingFilter(1))
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src/markov/presets/only_lidar_with_bn.py [83:111]:
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agent_params.network_wrappers['main'].batch_size = 128
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 = 50000
agent_params.network_wrappers['main'].learning_rate_decay_rate = 0.95

agent_params.algorithm.clip_likelihood_ratio_using_epsilon = 0.2
agent_params.algorithm.clipping_decay_schedule = LinearSchedule(1.0, 0, 1000000)
agent_params.algorithm.beta_entropy = 0.01  # also try 0.001
agent_params.algorithm.gae_lambda = 0.95
agent_params.algorithm.discount = 0.999
agent_params.algorithm.optimization_epochs = 10
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('front_camera', 'to_grayscale', ObservationRGBToYFilter())
SilverstoneInputFilter.add_observation_filter('front_camera', 'to_uint8', ObservationToUInt8Filter(0, 255))
SilverstoneInputFilter.add_observation_filter('front_camera', 'stacking', ObservationStackingFilter(1))
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