RL in Large Discrete Action Spaces - Wolpertinger Agent (#394)

* Currently this is specific to the case of discretizing a continuous action space. Can easily be adapted to other case by feeding the kNN otherwise, and removing the usage of a discretizing output action filter

rl_coach/presets/Mujoco_Wolpertinger.py

@@ -0,0 +1,57 @@
+from collections import OrderedDict
+
+from rl_coach.architectures.layers import Dense
+from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, EmbedderScheme
+from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps
+from rl_coach.environments.environment import SingleLevelSelection
+from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2
+from rl_coach.filters.action import BoxDiscretization
+from rl_coach.filters.filter import OutputFilter
+from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
+from rl_coach.graph_managers.graph_manager import ScheduleParameters
+from rl_coach.agents.wolpertinger_agent import WolpertingerAgentParameters
+
+####################
+# Graph Scheduling #
+####################
+schedule_params = ScheduleParameters()
+schedule_params.improve_steps = EnvironmentSteps(2000000)
+schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(20)
+schedule_params.evaluation_steps = EnvironmentEpisodes(1)
+schedule_params.heatup_steps = EnvironmentSteps(3000)
+
+#########
+# Agent #
+#########
+agent_params = WolpertingerAgentParameters()
+agent_params.network_wrappers['actor'].input_embedders_parameters['observation'].scheme = [Dense(400)]
+agent_params.network_wrappers['actor'].middleware_parameters.scheme = [Dense(300)]
+agent_params.network_wrappers['critic'].input_embedders_parameters['observation'].scheme = [Dense(400)]
+agent_params.network_wrappers['critic'].middleware_parameters.scheme = [Dense(300)]
+agent_params.network_wrappers['critic'].input_embedders_parameters['action'].scheme = EmbedderScheme.Empty
+agent_params.output_filter = \
+    OutputFilter(
+        action_filters=OrderedDict([
+            ('discretization', BoxDiscretization(num_bins_per_dimension=int(1e6)))
+        ]),
+        is_a_reference_filter=False
+    )
+
+###############
+# Environment #
+###############
+env_params = GymVectorEnvironment(level=SingleLevelSelection(mujoco_v2))
+
+########
+# Test #
+########
+preset_validation_params = PresetValidationParameters()
+preset_validation_params.test = True
+preset_validation_params.min_reward_threshold = 500
+preset_validation_params.max_episodes_to_achieve_reward = 1000
+preset_validation_params.reward_test_level = 'inverted_pendulum'
+preset_validation_params.trace_test_levels = ['inverted_pendulum']
+
+graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
+                                    schedule_params=schedule_params, vis_params=VisualizationParameters(),
+                                    preset_validation_params=preset_validation_params)