pre-release 0.10.0

rl_coach/presets/CartPole_NEC.py

@@ -0,0 +1,57 @@
+from rl_coach.agents.nec_agent import NECAgentParameters
+from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters
+from rl_coach.environments.environment import SelectedPhaseOnlyDumpMethod, MaxDumpMethod
+from rl_coach.environments.gym_environment import Atari, MujocoInputFilter
+from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
+from rl_coach.graph_managers.graph_manager import ScheduleParameters
+from rl_coach.memories.memory import MemoryGranularity
+from rl_coach.schedules import LinearSchedule
+
+from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, RunPhase
+from rl_coach.filters.reward.reward_rescale_filter import RewardRescaleFilter
+
+####################
+# Graph Scheduling #
+####################
+
+schedule_params = ScheduleParameters()
+schedule_params.improve_steps = TrainingSteps(10000000000)
+schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(10)
+schedule_params.evaluation_steps = EnvironmentEpisodes(1)
+schedule_params.heatup_steps = EnvironmentSteps(1300)
+
+#########
+# Agent #
+#########
+
+agent_params = NECAgentParameters()
+
+agent_params.network_wrappers['main'].learning_rate = 0.00025
+agent_params.exploration.epsilon_schedule = LinearSchedule(0.5, 0.1, 1000)
+agent_params.exploration.evaluation_epsilon = 0
+agent_params.algorithm.discount = 0.99
+agent_params.memory.max_size = (MemoryGranularity.Episodes, 200)
+agent_params.input_filter = MujocoInputFilter()
+agent_params.input_filter.add_reward_filter('rescale', RewardRescaleFilter(1/200.))
+
+###############
+# Environment #
+###############
+env_params = Atari()
+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 = 300
+
+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)