End-to-End Deep Reinforcement Learning for Lane Keeping Assist (1612.04340v1)

Abstract: Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes, but it has not yet been successfully used for automotive applications. There has recently been a revival of interest in the topic, however, driven by the ability of deep learning algorithms to learn good representations of the environment. Motivated by Google DeepMind's successful demonstrations of learning for games from Breakout to Go, we will propose different methods for autonomous driving using deep reinforcement learning. This is of particular interest as it is difficult to pose autonomous driving as a supervised learning problem as it has a strong interaction with the environment including other vehicles, pedestrians and roadworks. As this is a relatively new area of research for autonomous driving, we will formulate two main categories of algorithms: 1) Discrete actions category, and 2) Continuous actions category. For the discrete actions category, we will deal with Deep Q-Network Algorithm (DQN) while for the continuous actions category, we will deal with Deep Deterministic Actor Critic Algorithm (DDAC). In addition to that, We will also discover the performance of these two categories on an open source car simulator for Racing called (TORCS) which stands for The Open Racing car Simulator. Our simulation results demonstrate learning of autonomous maneuvering in a scenario of complex road curvatures and simple interaction with other vehicles. Finally, we explain the effect of some restricted conditions, put on the car during the learning phase, on the convergence time for finishing its learning phase.

End-to-End Deep Reinforcement Learning for Lane Keeping Assist

The paper "End-to-End Deep Reinforcement Learning for Lane Keeping Assist" examines the application of deep reinforcement learning (DRL) techniques in the context of autonomous vehicle control, specifically focusing on the lane-keeping assist function. Traditional supervised learning approaches in autonomous driving face challenges due to the dynamic interaction with the environment, which necessitates adaptive and responsive learning systems. This paper explores DRL methodologies to address these challenges, utilizing both discrete and continuous action frameworks: Deep Q-Networks (DQN) for discrete actions and Deep Deterministic Actor Critic (DDAC) for continuous actions.

Algorithms and Methodologies

The authors structure the problem space into two main categories and examine the performance of each using an open-source car racing simulator, TORCS. This choice enables the simulation of complex road scenarios and basic vehicle interactions.

• Deep Q-Networks (DQN): This method employs Q-learning with deep neural networks (DNN) to approximate Q-values. The algorithm transforms scenarios with discrete state-action spaces into an end-to-end learning problem to minimize the Mean Square Error (MSE) of the Q-values. Nevertheless, DQN is limited to discrete actions, which can result in abrupt control responses.
• Deep Deterministic Actor Critic (DDAC): In contrast to DQN, DDAC handles continuous action spaces. It employs actor-critic methodologies where the actor network dictates policy mapping from states to actions, and the critic network evaluates these actions. The continuous nature of DDAC's action space allows for smoother control, representing a significant advantage in maneuvering scenarios involving curvatures and turns.

Further, the paper explores additional DRL techniques such as deep recurrent reinforcement learning and deep attention reinforcement learning, highlighting their relevance in situations where full environmental visibility is absent, and trailed objects need continuous tracking.

Experimental Setup and Results

Utilizing TORCS, the simulation setup integrates sensor input data, which includes track position and car speed, with the intention of training the networks end-to-end. Both DQN and DDAC demonstrate successful lane-keeping capabilities with DQN showing inefficiencies due to action discretization, particularly in curves where abrupt actions are noticeable. Conversely, DDAC's continuous policy gradient offers improved smoothness in vehicle control, reflecting its superiority in handling both straight and curved road sections proficiently.

Implications and Future Directions

The exploration of termination conditions within the simulation highlights the influence of constraint setting on learning efficiency. Results indicate that unconstrained learning leads to faster convergence, albeit with a risk of settling into local minimum points. These findings underscore the need for fine-tuning reward structures and termination parameters to optimize DRL systems in real-world autonomous driving applications.

The paper provides empirical evidence for the effectiveness of DRL in lane-keeping. It opens up considerations for integrating DRL across broader autonomous driving functions. Future efforts might focus on enhancing the robustness of DRL models to handle more complex interactions, advanced sensor fusion techniques, and optimizing training algorithms for improved performance in unpredictable driving environments, pushing towards real-world applicability in autonomous systems.