def __init__(self): super().__init__() self.input_embedders_parameters = {'observation': InputEmbedderParameters(activation_function='leaky_relu'), 'measurements': InputEmbedderParameters(activation_function='leaky_relu'), 'goal': InputEmbedderParameters(activation_function='leaky_relu')} self.input_embedders_parameters['observation'].scheme = [ Conv2d(32, 8, 4), Conv2d(64, 4, 2), Conv2d(64, 3, 1), Dense(512), ] self.input_embedders_parameters['measurements'].scheme = [ Dense(128), Dense(128), Dense(128), ] self.input_embedders_parameters['goal'].scheme = [ Dense(128), Dense(128), Dense(128), ] self.middleware_parameters = FCMiddlewareParameters(activation_function='leaky_relu', scheme=MiddlewareScheme.Empty) self.heads_parameters = [MeasurementsPredictionHeadParameters(activation_function='leaky_relu')] self.async_training = False self.batch_size = 64 self.adam_optimizer_beta1 = 0.95
def __init__(self): super().__init__() self.input_embedders_parameters = {'observation': InputEmbedderParameters()} self.middleware_parameters = FCMiddlewareParameters() self.heads_parameters = [DNDQHeadParameters()] self.optimizer_type = 'Adam' self.should_get_softmax_probabilities = False
def __init__(self): super().__init__() self.input_embedders_parameters = {'observation': InputEmbedderParameters()} self.middleware_parameters = FCMiddlewareParameters() self.heads_parameters = [VHeadParameters(loss_weight=0.5), PolicyHeadParameters(loss_weight=1.0)] self.optimizer_type = 'Adam' self.clip_gradients = 40.0 self.async_training = True
def __init__(self): super().__init__() self.input_embedders_parameters = { 'observation': InputEmbedderParameters() } self.middleware_parameters = FCMiddlewareParameters() self.heads_parameters = [DNDQHeadParameters()] self.optimizer_type = 'Adam'
def __init__(self): super().__init__() self.input_embedders_parameters = { 'observation': InputEmbedderParameters() } self.middleware_parameters = FCMiddlewareParameters() self.heads_parameters = [PolicyHeadParameters()] self.async_training = True
def __init__(self): super().__init__() self.input_embedders_parameters = {'observation': InputEmbedderParameters()} self.middleware_parameters = FCMiddlewareParameters(scheme=MiddlewareScheme.Medium) self.heads_parameters = [RegressionHeadParameters()] self.optimizer_type = 'Adam' self.batch_size = 32 self.replace_mse_with_huber_loss = False self.create_target_network = False
def __init__(self): super().__init__() self.input_embedders_parameters = {'observation': InputEmbedderParameters()} self.middleware_parameters = FCMiddlewareParameters() self.heads_parameters = [QHeadParameters()] self.optimizer_type = 'Adam' self.async_training = True self.shared_optimizer = True self.create_target_network = True
def test_fc_middleware(): params = FCMiddlewareParameters(scheme=MiddlewareScheme.Medium) mid = FCMiddleware(params=params) mid.initialize() embedded_data = mx.nd.random.uniform(low=0, high=1, shape=(10, 100)) output = mid(embedded_data) assert output.ndim == 2 # since last block was flatten assert output.shape[0] == 10 # since batch_size is 10 assert output.shape[ 1] == 512 # since last layer of middleware (middle scheme) had 512 units
def __init__(self): super().__init__() self.input_embedders_parameters = {'observation': InputEmbedderParameters(activation_function='leaky_relu', input_rescaling={'image': 1.0})} self.middleware_parameters = FCMiddlewareParameters(scheme=MiddlewareScheme.Empty) self.heads_parameters = [RNDHeadParameters()] self.create_target_network = False self.optimizer_type = 'Adam' self.batch_size = 100 self.learning_rate = 0.0001 self.should_get_softmax_probabilities = False
def __init__(self): super().__init__() self.input_embedders_parameters = { 'observation': InputEmbedderParameters() } self.middleware_parameters = FCMiddlewareParameters( activation_function='none') self.heads_parameters = [RNDHeadParameters()] self.optimizer_type = 'Adam' self.clip_gradients = None self.create_target_network = False
def __init__(self): super().__init__() self.input_embedders_parameters = {'observation': InputEmbedderParameters(scheme=EmbedderScheme.Empty)} self.middleware_parameters = FCMiddlewareParameters(scheme=MiddlewareScheme.Empty) self.heads_parameters = [SACQHeadParameters()] # SACQHeadParameters includes the topology of the head self.rescale_gradient_from_head_by_factor = [1] self.optimizer_type = 'Adam' self.batch_size = 256 self.async_training = False self.learning_rate = 0.0003 self.create_target_network = False
def __init__(self): super().__init__() self.input_embedders_parameters = {'observation': InputEmbedderParameters(activation_function='relu')} self.middleware_parameters = FCMiddlewareParameters(activation_function='relu') self.heads_parameters = [SACPolicyHeadParameters()] self.rescale_gradient_from_head_by_factor = [1] self.optimizer_type = 'Adam' self.batch_size = 256 self.async_training = False self.learning_rate = 0.0003 self.create_target_network = False self.l2_regularization = 0 # weight decay regularization. not used in the original paper
def __init__(self): super().__init__() self.input_embedders_parameters = {'observation': InputEmbedderParameters(activation_function='relu')} self.middleware_parameters = FCMiddlewareParameters(activation_function='relu') self.heads_parameters = [VHeadParameters(initializer='xavier')] self.rescale_gradient_from_head_by_factor = [1] self.optimizer_type = 'Adam' self.batch_size = 256 self.async_training = False self.learning_rate = 0.0003 # 3e-4 see appendix D in the paper # tau is set in SoftActorCriticAlgorithmParameters.rate_for_copying_weights_to_target self.create_target_network = True
def __init__(self): super().__init__() self.input_embedders_parameters = { 'observation': InputEmbedderParameters(activation_function='tanh') } self.middleware_parameters = FCMiddlewareParameters( activation_function='tanh') self.heads_parameters = [VHeadParameters()] self.async_training = True self.l2_regularization = 0 self.create_target_network = True self.batch_size = 128
def __init__(self): super().__init__() self.input_embedders_parameters = { 'observation': InputEmbedderParameters(batchnorm=True) } self.middleware_parameters = FCMiddlewareParameters(batchnorm=True) self.heads_parameters = [DDPGActorHeadParameters()] self.optimizer_type = 'Adam' self.batch_size = 64 self.async_training = False self.learning_rate = 0.0001 self.create_target_network = True self.shared_optimizer = True self.scale_down_gradients_by_number_of_workers_for_sync_training = False
def __init__(self, num_q_networks): super().__init__() self.input_embedders_parameters = {'observation': InputEmbedderParameters(), 'action': InputEmbedderParameters(scheme=EmbedderScheme.Shallow)} self.middleware_parameters = FCMiddlewareParameters(num_streams=num_q_networks) self.heads_parameters = [TD3VHeadParameters()] self.optimizer_type = 'Adam' self.adam_optimizer_beta2 = 0.999 self.optimizer_epsilon = 1e-8 self.batch_size = 100 self.async_training = False self.learning_rate = 0.001 self.create_target_network = True self.shared_optimizer = True self.scale_down_gradients_by_number_of_workers_for_sync_training = False
def __init__(self): super().__init__() self.input_embedders_parameters = {'observation': InputEmbedderParameters(activation_function='tanh')} self.middleware_parameters = FCMiddlewareParameters(activation_function='tanh') self.heads_parameters = [VHeadParameters(), PPOHeadParameters()] self.batch_size = 64 self.optimizer_type = 'Adam' self.clip_gradients = None self.use_separate_networks_per_head = True self.async_training = False self.l2_regularization = 0 # The target network is used in order to freeze the old policy, while making updates to the new one # in train_network() self.create_target_network = True self.shared_optimizer = True self.scale_down_gradients_by_number_of_workers_for_sync_training = True
def create_middle_embedder(scheme_dict, embedder_type, activation_function): """Creates rl coach middleware scheme_dict - Dictionary containing all the information required by the scheme creation methods defined above. embedder_type - String indicating desired inputembedder type, available types are defined in SCHEME_TYPE activation_function - Desired activationfunction for the embdedder """ try: if not ActivationFunctions.has_activation_function(activation_function): raise Exception("Invalid activation function for middleware") scheme = SCHEME_TYPE[embedder_type](scheme_dict) return FCMiddlewareParameters(scheme=scheme, activation_function=activation_function) except KeyError as err: raise Exception("Middleware, key {} not found".format(err.args[0])) except Exception as err: raise Exception("Error while creating middleware: {}".format(err))
# Agent Params # ################ agent_params = DDPGAgentParameters() # actor actor_network = agent_params.network_wrappers['actor'] actor_network.learning_rate = 0.001 actor_network.batch_size = 256 actor_network.optimizer_epsilon = 1e-08 actor_network.adam_optimizer_beta1 = 0.9 actor_network.adam_optimizer_beta2 = 0.999 actor_network.input_embedders_parameters = { 'observation': InputEmbedderParameters(scheme=EmbedderScheme.Empty), 'desired_goal': InputEmbedderParameters(scheme=EmbedderScheme.Empty) } actor_network.middleware_parameters = FCMiddlewareParameters(scheme=[Dense(256), Dense(256), Dense(256)]) actor_network.heads_parameters[0].batchnorm = False # critic critic_network = agent_params.network_wrappers['critic'] critic_network.learning_rate = 0.001 critic_network.batch_size = 256 critic_network.optimizer_epsilon = 1e-08 critic_network.adam_optimizer_beta1 = 0.9 critic_network.adam_optimizer_beta2 = 0.999 critic_network.input_embedders_parameters = { 'action': InputEmbedderParameters(scheme=EmbedderScheme.Empty), 'desired_goal': InputEmbedderParameters(scheme=EmbedderScheme.Empty), 'observation': InputEmbedderParameters(scheme=EmbedderScheme.Empty) } critic_network.middleware_parameters = FCMiddlewareParameters(scheme=[Dense(256), Dense(256), Dense(256)])
def __init__(self): super().__init__() self.heads_parameters = [RainbowQHeadParameters()] self.middleware_parameters = FCMiddlewareParameters( scheme=MiddlewareScheme.Empty)
activation_function='none'), 'lidar': InputEmbedderParameters( scheme=[ Dense(256), BatchnormActivationDropout(activation_function='relu', dropout_rate=0.5), Dense(256), BatchnormActivationDropout(activation_function='relu', dropout_rate=0.5) ], activation_function='none') # we define the activation function for each layer explicitly } agent_params.network_wrappers['main'].middleware_parameters = \ FCMiddlewareParameters( scheme=[ Dense(256), BatchnormActivationDropout(activation_function='relu', dropout_rate=0.5), Dense(128), BatchnormActivationDropout(activation_function='relu', dropout_rate=0.5) ], activation_function='none' ) agent_params.network_wrappers['main'].learning_rate = 0.0003 # agent_params.network_wrappers['main'].input_embedders_parameters['front_camera'].activation_function = 'relu' # agent_params.network_wrappers['main'].middleware_parameters.activation_function = 'relu' 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)
schedule_params = ScheduleParameters() schedule_params.improve_steps = TrainingSteps(10000000000) schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(100) schedule_params.evaluation_steps = EnvironmentEpisodes(3) schedule_params.heatup_steps = EnvironmentSteps(0) ######### # Agent # ######### agent_params = ActorCriticAgentParameters() agent_params.algorithm.apply_gradients_every_x_episodes = 1 agent_params.algorithm.num_steps_between_gradient_updates = 20 agent_params.algorithm.beta_entropy = 0.05 agent_params.network_wrappers['main'].middleware_parameters = FCMiddlewareParameters() agent_params.network_wrappers['main'].learning_rate = 0.0001 ############### # Environment # ############### env_params = Atari(level=SingleLevelSelection(atari_deterministic_v4)) ######## # Test # ######## preset_validation_params = PresetValidationParameters() preset_validation_params.trace_test_levels = ['breakout', 'pong', 'space_invaders'] graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params, schedule_params=schedule_params, vis_params=VisualizationParameters(),
InputEmbedderParameters( scheme=[Conv2d(32, 8, 4), Conv2d(32, 4, 2), Conv2d(64, 4, 2)], activation_function='relu', dropout_rate=0.3), 'LIDAR': InputEmbedderParameters(scheme=[Dense(64), Dense(32)], activation_function='relu', dropout_rate=0.3) } agent_params.network_wrappers['main'].middleware_parameters = \ FCMiddlewareParameters( scheme=[ Dense(256) ], activation_function='relu', dropout_rate=0.3 ) 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
Conv2d(64, 3, 1), Conv2d(64, 3, 2), Conv2d(128, 3, 1) ]) agent_params.network_wrappers['main'].input_embedders_parameters = { 'observation': input_embedder_params } agent_params.network_wrappers['constant'].input_embedders_parameters = { 'observation': input_embedder_params } agent_params.network_wrappers['predictor'].input_embedders_parameters = { 'observation': input_embedder_params } middleware_scheme = MiddlewareScheme.Medium agent_params.network_wrappers[ 'main'].middleware_parameters = FCMiddlewareParameters( scheme=middleware_scheme) agent_params.network_wrappers[ 'constant'].middleware_parameters = FCMiddlewareParameters( activation_function='none', scheme=middleware_scheme) agent_params.network_wrappers[ 'predictor'].middleware_parameters = FCMiddlewareParameters( activation_function='none', scheme=middleware_scheme) agent_params.network_wrappers['main'].heads_parameters = [ DuelingQHeadParameters() ] # ER agent_params.memory.max_size = (MemoryGranularity.Transitions, 4096) ################ # Environment #
dropout_rate=0.5), Dense(128), BatchnormActivationDropout(activation_function='tanh', dropout_rate=0.5) ], activation_function= 'none' # we define the activation function for each layer explicitly ) } # simple fc middleware agent_params.network_wrappers['main'].middleware_parameters = \ FCMiddlewareParameters( scheme=[ Dense(512), BatchnormActivationDropout(activation_function='tanh', dropout_rate=0.5) ], activation_function='none' ) # output branches agent_params.network_wrappers['main'].heads_parameters = [ RegressionHeadParameters( scheme=[ Dense(256), BatchnormActivationDropout(activation_function='tanh', dropout_rate=0.5), Dense(256), BatchnormActivationDropout(activation_function='tanh') ], num_output_head_copies=4 # follow lane, left, right, straight