- a -

• action : training_node.TrainingNode
• actions : reinforcement_learning_node.ReinforcementLearningNode
• angle : simulation_tools.track.TrackPosition
• anti_windup : wallfollowing.PIDController

- c -

• car_orientation : training_node.TrainingNode
• car_position : training_node.TrainingNode
• center : circle.Circle , simulation_tools.lap_timer.Area
• checkpoints : simulation_tools.lap_timer.Timer
• cumulative_distance : simulation_tools.track.Track
• cumulative_reward : training_node.TrainingNode
• cumulative_reward_history : training_node.TrainingNode
• current_driver : train.TrainingNode
• current_episode_rewards : train_policy_gradient.PolicyGradientTrainingNode

- d -

• d : wallfollowing.PIDController
• distance_to_center : simulation_tools.track.TrackPosition
• drive_forward : training_node.TrainingNode
• drive_parameters_publisher : reinforcement_learning_node.ReinforcementLearningNode

- e -

• episode_count : training_node.TrainingNode
• episode_length : train.TrainingNode , training_node.TrainingNode
• episode_length_history : training_node.TrainingNode
• episode_result_publisher : training_node.TrainingNode
• episode_start_time_real : training_node.TrainingNode
• episode_start_time_sim : training_node.TrainingNode
• extents : simulation_tools.lap_timer.Area

- f -

• fc1 : parameters_q_learning.NeuralQEstimator
• fc2 : parameters_q_learning.NeuralQEstimator
• fc3 : parameters_q_learning.NeuralQEstimator
• fitness : neural_car_driver.NeuralCarDriver
• forward : simulation_tools.track.Track

- g -

• generation : train.TrainingNode

- h -

• history : simulation_tools.lap_timer.Timer

- i -

• i : wallfollowing.PIDController
• integral : wallfollowing.PIDController
• is_terminal_step : train.TrainingNode , training_node.TrainingNode

- l -

• lap_time_publisher : simulation_tools.lap_timer.Timer
• laser_sample_count : reinforcement_learning_node.ReinforcementLearningNode
• layers : neural_car_driver.NeuralCarDriver , parameters_policy_gradient.Policy
• length : simulation_tools.track.Track

- m -

• m_angle1 : Wall
• m_angle2 : Wall
• m_range1 : Wall
• m_range2 : Wall
• max_episode_length : training_node.TrainingNode
• memory : train_q_learning.QLearningTrainingNode

- n -

• name : simulation_tools.lap_timer.Timer
• names : wallfollowing.Parameters
• net_output_debug_string : train_q_learning.QLearningTrainingNode , training_node.TrainingNode
• next_checkpoint : simulation_tools.lap_timer.Timer

- o -

• optimization_step_count : train_q_learning.QLearningTrainingNode
• optimizer : training_node.TrainingNode

- p -

• p : wallfollowing.PIDController
• point : simulation_tools.track.TrackPosition
• points : simulation_tools.track.Track
• policy : drive_policy_gradient.PolicyGradientDrivingNode , drive_q_learning.QLearningDrivingNode , training_node.TrainingNode
• policy_history : train_policy_gradient.PolicyGradientTrainingNode
• population : train.TrainingNode
• previous_error : wallfollowing.PIDController

- r -

• radius : circle.Circle
• right : simulation_tools.track.Track

- s -

• scan_indices : neural_car_driver.NeuralCarDriver , reinforcement_learning_node.ReinforcementLearningNode , train.TrainingNode
• segment : simulation_tools.track.TrackPosition
• segment_distance : simulation_tools.track.TrackPosition
• segment_length : simulation_tools.track.Track
• segment_progress : simulation_tools.track.TrackPosition
• size : simulation_tools.track.Track
• start : simulation_tools.lap_timer.Timer
• state : training_node.TrainingNode
• steps_with_wrong_orientation : training_node.TrainingNode

- t -

• test : train.TrainingNode
• total_distance : simulation_tools.track.TrackPosition
• total_progress : simulation_tools.track.TrackPosition
• total_step_count : training_node.TrainingNode
• total_velocity : neural_car_driver.NeuralCarDriver

- u -

• untested_population : train.TrainingNode