Adding should_train helper and should_train in graph_manager

rl_coach/presets/CartPole_PPO.py

@@ -0,0 +1,75 @@
+from rl_coach.agents.clipped_ppo_agent import ClippedPPOAgentParameters
+from rl_coach.architectures.tensorflow_components.architecture import Dense
+from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters
+from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, RunPhase
+from rl_coach.environments.environment import MaxDumpMethod, SelectedPhaseOnlyDumpMethod, SingleLevelSelection
+from rl_coach.environments.gym_environment import Mujoco, mujoco_v2, MujocoInputFilter
+from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
+from rl_coach.exploration_policies.e_greedy import EGreedyParameters
+from rl_coach.filters.observation.observation_normalization_filter import ObservationNormalizationFilter
+from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
+from rl_coach.graph_managers.graph_manager import ScheduleParameters
+from rl_coach.schedules import LinearSchedule
+
+####################
+# Graph Scheduling #
+####################
+
+schedule_params = ScheduleParameters()
+schedule_params.improve_steps = TrainingSteps(10000000)
+schedule_params.steps_between_evaluation_periods = EnvironmentSteps(2048)
+schedule_params.evaluation_steps = EnvironmentEpisodes(5)
+schedule_params.heatup_steps = EnvironmentSteps(0)
+
+#########
+# Agent #
+#########
+agent_params = ClippedPPOAgentParameters()
+
+
+agent_params.network_wrappers['main'].learning_rate = 0.0003
+agent_params.network_wrappers['main'].input_embedders_parameters['observation'].activation_function = 'tanh'
+agent_params.network_wrappers['main'].input_embedders_parameters['observation'].scheme = [Dense([64])]
+agent_params.network_wrappers['main'].middleware_parameters.scheme = [Dense([64])]
+agent_params.network_wrappers['main'].middleware_parameters.activation_function = 'tanh'
+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.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
+agent_params.algorithm.gae_lambda = 0.95
+agent_params.algorithm.discount = 0.99
+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 = EnvironmentSteps(2048)
+
+# agent_params.input_filter = MujocoInputFilter()
+agent_params.exploration = EGreedyParameters()
+agent_params.exploration.epsilon_schedule = LinearSchedule(1.0, 0.01, 10000)
+# agent_params.pre_network_filter = MujocoInputFilter()
+agent_params.pre_network_filter.add_observation_filter('observation', 'normalize_observation',
+                                                       ObservationNormalizationFilter(name='normalize_observation'))
+
+###############
+# 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 = 250
+
+graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
+                                    schedule_params=schedule_params, vis_params=vis_params,
+                                    preset_validation_params=preset_validation_params)