Enabling-more-agents-for-Batch-RL-and-cleanup (#258)

allowing for the last training batch drawn to be smaller than batch_size + adding support for more agents in BatchRL by adding softmax with temperature to the corresponding heads + adding a CartPole_QR_DQN preset with a golden test + cleanups

rl_coach/presets/CartPole_QR_DQN.py

@@ -0,0 +1,55 @@
+from rl_coach.agents.qr_dqn_agent import QuantileRegressionDQNAgentParameters
+from rl_coach.agents.rainbow_dqn_agent import RainbowDQNAgentParameters
+from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters
+from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps
+from rl_coach.environments.gym_environment import GymVectorEnvironment
+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
+
+####################
+# 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(1000)
+
+#########
+# Agent #
+#########
+agent_params = QuantileRegressionDQNAgentParameters()
+
+# DQN params
+agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(100)
+agent_params.algorithm.discount = 0.99
+agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(1)
+agent_params.algorithm.atoms = 50
+# NN configuration
+agent_params.network_wrappers['main'].learning_rate = 0.0005
+
+# ER size
+agent_params.memory.max_size = (MemoryGranularity.Transitions, 40000)
+
+# E-Greedy schedule
+agent_params.exploration.epsilon_schedule = LinearSchedule(1.0, 0.01, 10000)
+
+################
+#  Environment #
+################
+env_params = GymVectorEnvironment(level='CartPole-v0')
+
+########
+# 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=VisualizationParameters(),
+                                    preset_validation_params=preset_validation_params)