pre-release 0.10.0

rl_coach/presets/BitFlip_DQN.py

@@ -0,0 +1,68 @@
+from rl_coach.architectures.tensorflow_components.architecture import Dense
+from rl_coach.base_parameters import VisualizationParameters, EmbedderScheme, InputEmbedderParameters, \
+    PresetValidationParameters
+from rl_coach.environments.gym_environment import Mujoco
+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 ConstantSchedule
+
+from rl_coach.agents.dqn_agent import DQNAgentParameters
+from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
+
+bit_length = 8
+
+####################
+# Graph Scheduling #
+####################
+schedule_params = ScheduleParameters()
+schedule_params.improve_steps = TrainingSteps(400000)
+schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(16 * 50)  # 50 cycles
+schedule_params.evaluation_steps = EnvironmentEpisodes(10)
+schedule_params.heatup_steps = EnvironmentSteps(0)
+
+#########
+# Agent #
+#########
+agent_params = DQNAgentParameters()
+agent_params.network_wrappers['main'].learning_rate = 0.001
+agent_params.network_wrappers['main'].batch_size = 128
+agent_params.network_wrappers['main'].middleware_parameters.scheme = [Dense([256])]
+agent_params.network_wrappers['main'].input_embedders_parameters = {
+    'state': InputEmbedderParameters(scheme=EmbedderScheme.Empty),
+    'desired_goal': InputEmbedderParameters(scheme=EmbedderScheme.Empty)
+}
+agent_params.algorithm.discount = 0.98
+agent_params.algorithm.num_consecutive_playing_steps = EnvironmentEpisodes(16)
+agent_params.algorithm.num_consecutive_training_steps = 40
+agent_params.algorithm.num_steps_between_copying_online_weights_to_target = TrainingSteps(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 = 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()
+
+########
+# 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=vis_params,
+                                    preset_validation_params=preset_validation_params)
+
+
+# self.algorithm.add_intrinsic_reward_for_reaching_the_goal = False
+