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)
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
preset_validation_params.min_reward_threshold = -15
preset_validation_params.max_episodes_to_achieve_reward = 10000
'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))