# Agent #
#########
agent_params = PolicyGradientsAgentParameters()
agent_params.algorithm.apply_gradients_every_x_episodes = 5
agent_params.algorithm.num_steps_between_gradient_updates = 20000
agent_params.network_wrappers['main'].learning_rate = 0.0005
agent_params.input_filter = MujocoInputFilter()
agent_params.input_filter.add_reward_filter('rescale',
RewardRescaleFilter(1 / 20.))
agent_params.input_filter.add_observation_filter(
'observation', 'normalize', ObservationNormalizationFilter())
###############
# Environment #
###############
env_params = Mujoco()
env_params.level = "InvertedPendulum-v2"
vis_params = VisualizationParameters()
vis_params.video_dump_methods = [
SelectedPhaseOnlyDumpMethod(RunPhase.TEST),
MaxDumpMethod()
]
vis_params.dump_mp4 = False
graph_manager = BasicRLGraphManager(agent_params=agent_params,
env_params=env_params,
schedule_params=schedule_params,
vis_params=vis_params)
agent_params.memory = EpisodicHindsightExperienceReplayParameters()
agent_params.memory.hindsight_goal_selection_method = HindsightGoalSelectionMethod.Final
agent_params.memory.hindsight_transitions_per_regular_transition = 1
agent_params.memory.goals_space = GoalsSpace(
goal_name='state',
reward_type=ReachingGoal(distance_from_goal_threshold=0,
goal_reaching_reward=0,
default_reward=-1),
distance_metric=GoalsSpace.DistanceMetric.Euclidean)
###############
# Environment #
###############
env_params = Mujoco()
env_params.level = 'rl_coach.environments.toy_problems.bit_flip:BitFlip'
env_params.additional_simulator_parameters = {
'bit_length': bit_length,
'mean_zero': True
}
env_params.custom_reward_threshold = -bit_length + 1
vis_params = VisualizationParameters()
# currently no tests for this preset as the max reward can be accidently achieved. will be fixed with trace based tests.
########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
#########
agent_params = NStepQAgentParameters()
agent_params.algorithm.discount = 0.99
agent_params.network_wrappers['main'].learning_rate = 0.0001
agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(
100)
agent_params.input_filter = MujocoInputFilter()
agent_params.input_filter.add_reward_filter('rescale',
RewardRescaleFilter(1 / 200.))
###############
# Environment #
###############
env_params = Mujoco()
env_params.level = 'CartPole-v0'
vis_params = VisualizationParameters()
vis_params.video_dump_methods = [
SelectedPhaseOnlyDumpMethod(RunPhase.TEST),
MaxDumpMethod()
]
vis_params.dump_mp4 = False
########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
preset_validation_params.min_reward_threshold = 150
preset_validation_params.max_episodes_to_achieve_reward = 200
schedule_params.heatup_steps = EnvironmentSteps(1000) ######### # Agent # ######### agent_params = NAFAgentParameters() agent_params.network_wrappers['main'].input_embedders_parameters['observation'].scheme = [Dense([200])] agent_params.network_wrappers['main'].middleware_parameters.scheme = [Dense([200])] agent_params.network_wrappers['main'].clip_gradients = 1000 agent_params.network_wrappers['main'].gradients_clipping_method = GradientClippingMethod.ClipByValue ############### # Environment # ############### env_params = Mujoco() env_params.level = SingleLevelSelection(mujoco_v2) vis_params = VisualizationParameters() vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()] vis_params.dump_mp4 = False # this preset is currently broken - no test ######## # Test # ######## preset_validation_params = PresetValidationParameters() # preset_validation_params.test = True # preset_validation_params.min_reward_threshold = 200
agent_params.network_wrappers[
'main'].num_output_head_copies = num_output_head_copies
agent_params.network_wrappers['main'].rescale_gradient_from_head_by_factor = [
1.0 / num_output_head_copies
] * num_output_head_copies
agent_params.exploration = UCBParameters()
agent_params.exploration.bootstrapped_data_sharing_probability = 1.0
agent_params.exploration.architecture_num_q_heads = num_output_head_copies
agent_params.exploration.epsilon_schedule = ConstantSchedule(0)
agent_params.exploration.lamb = 10
agent_params.input_filter = NoInputFilter()
agent_params.output_filter = NoOutputFilter()
###############
# Environment #
###############
env_params = Mujoco()
env_params.level = 'rl_coach.environments.toy_problems.exploration_chain:ExplorationChain'
env_params.additional_simulator_parameters = {
'chain_length': N,
'max_steps': N + 7
}
vis_params = VisualizationParameters()
graph_manager = BasicRLGraphManager(agent_params=agent_params,
env_params=env_params,
schedule_params=schedule_params,
vis_params=vis_params)
'action': InputEmbedderParameters(scheme=EmbedderScheme.Empty),
'desired_goal': InputEmbedderParameters(scheme=EmbedderScheme.Empty)}
bottom_critic.embedding_merger_type = EmbeddingMergerType.Concat
bottom_critic.middleware_parameters.scheme = [Dense([64])] * 3
bottom_critic.learning_rate = 0.001
bottom_critic.batch_size = 4096
agents_params = [top_agent_params, bottom_agent_params]
###############
# Environment #
###############
time_limit = 1000
env_params = Mujoco()
env_params.level = "rl_coach.environments.mujoco.pendulum_with_goals:PendulumWithGoals"
env_params.additional_simulator_parameters = {"time_limit": time_limit,
"random_goals_instead_of_standing_goal": False,
"polar_coordinates": polar_coordinates,
"goal_reaching_thresholds": distance_from_goal_threshold}
env_params.frame_skip = 10
env_params.custom_reward_threshold = -time_limit + 1
vis_params = VisualizationParameters()
vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST)]
vis_params.dump_mp4 = False
vis_params.native_rendering = False
graph_manager = HACGraphManager(agents_params=agents_params, env_params=env_params,
schedule_params=schedule_params, vis_params=vis_params,
consecutive_steps_to_run_non_top_levels=EnvironmentSteps(40))
agent_params.input_filter = MujocoInputFilter()
agent_params.input_filter.add_observation_filter('observation', 'clipping', ObservationClippingFilter(-200, 200))
agent_params.pre_network_filter = MujocoInputFilter()
agent_params.pre_network_filter.add_observation_filter('observation', 'normalize_observation',
ObservationNormalizationFilter(name='normalize_observation'))
agent_params.pre_network_filter.add_observation_filter('achieved_goal', 'normalize_achieved_goal',
ObservationNormalizationFilter(name='normalize_achieved_goal'))
agent_params.pre_network_filter.add_observation_filter('desired_goal', 'normalize_desired_goal',
ObservationNormalizationFilter(name='normalize_desired_goal'))
###############
# Environment #
###############
env_params = Mujoco()
env_params.level = SingleLevelSelection(fetch_v1)
env_params.custom_reward_threshold = -49
vis_params = VisualizationParameters()
vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()]
vis_params.dump_mp4 = False
########
# Test #
########
preset_validation_params = PresetValidationParameters()
# preset_validation_params.test = True
# preset_validation_params.min_reward_threshold = 200
# preset_validation_params.max_episodes_to_achieve_reward = 600
# preset_validation_params.reward_test_level = 'inverted_pendulum'