40)
agent_params.algorithm.rate_for_copying_weights_to_target = 0.05
agent_params.memory.max_size = (MemoryGranularity.Transitions, 10**6)
agent_params.exploration.epsilon_schedule = ConstantSchedule(0.2)
agent_params.exploration.evaluation_epsilon = 0
###############
# Environment #
###############
env_params = GymVectorEnvironment(
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
########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.test = True
preset_validation_params.min_reward_threshold = -7.9
preset_validation_params.max_episodes_to_achieve_reward = 10000
graph_manager = BasicRLGraphManager(
agent_params=agent_params,
env_params=env_params,
schedule_params=schedule_params,
vis_params=VisualizationParameters(),
preset_validation_params=preset_validation_params)
agent_params.exploration.continuous_exploration_policy_parameters.evaluation_noise = 0
agent_params.input_filter = InputFilter()
agent_params.input_filter.add_observation_filter('observation', 'clipping', ObservationClippingFilter(-200, 200))
agent_params.pre_network_filter = InputFilter()
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 = GymVectorEnvironment(level=SingleLevelSelection(fetch_v1))
env_params.custom_reward_threshold = -49
########
# Test #
########
preset_validation_params = PresetValidationParameters()
preset_validation_params.trace_test_levels = ['slide', 'pick_and_place', 'push', 'reach']
graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
schedule_params=schedule_params, vis_params=VisualizationParameters(),
preset_validation_params=preset_validation_params)
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 = GymVectorEnvironment(
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.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))