using the CoRL2017 experiment suite for CARLA_CIL

rl_coach/presets/CARLA_DDPG.py

@@ -0,0 +1,39 @@
+from rl_coach.agents.ddpg_agent import DDPGAgentParameters
+from rl_coach.base_parameters import VisualizationParameters
+from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, RunPhase
+from rl_coach.environments.carla_environment import CarlaEnvironmentParameters
+from rl_coach.environments.environment import MaxDumpMethod, SelectedPhaseOnlyDumpMethod
+from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
+from rl_coach.graph_managers.graph_manager import ScheduleParameters
+
+####################
+# Graph Scheduling #
+####################
+schedule_params = ScheduleParameters()
+schedule_params.improve_steps = TrainingSteps(10000000000)
+schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(20)
+schedule_params.evaluation_steps = EnvironmentEpisodes(1)
+schedule_params.heatup_steps = EnvironmentSteps(1000)
+
+#########
+# Agent #
+#########
+agent_params = DDPGAgentParameters()
+agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(4)
+agent_params.network_wrappers['actor'].input_embedders_parameters['forward_camera'] = \
+    agent_params.network_wrappers['actor'].input_embedders_parameters.pop('observation')
+agent_params.network_wrappers['critic'].input_embedders_parameters['forward_camera'] = \
+    agent_params.network_wrappers['critic'].input_embedders_parameters.pop('observation')
+
+###############
+# Environment #
+###############
+env_params = CarlaEnvironmentParameters()
+env_params.level = 'town1'
+
+vis_params = VisualizationParameters()
+vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()]
+vis_params.dump_mp4 = False
+
+graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params,
+                                    schedule_params=schedule_params, vis_params=vis_params)