Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving
This work addresses the problem of enhancing safety and performance for autonomous driving systems, though it appears incremental as it builds on existing reward shaping methods by adding risk awareness.
The paper tackled the challenge of designing reward functions for reinforcement learning agents in autonomous driving by incorporating risk-aware reward shaping to penalize risky behaviors, resulting in improved training and test performance as demonstrated in a simulation study with OpenAI Gym.
Reinforcement learning (RL) is an effective approach to motion planning in autonomous driving, where an optimal driving policy can be automatically learned using the interaction data with the environment. Nevertheless, the reward function for an RL agent, which is significant to its performance, is challenging to be determined. The conventional work mainly focuses on rewarding safe driving states but does not incorporate the awareness of risky driving behaviors of the vehicles. In this paper, we investigate how to use risk-aware reward shaping to leverage the training and test performance of RL agents in autonomous driving. Based on the essential requirements that prescribe the safety specifications for general autonomous driving in practice, we propose additional reshaped reward terms that encourage exploration and penalize risky driving behaviors. A simulation study in OpenAI Gym indicates the advantage of risk-aware reward shaping for various RL agents. Also, we point out that proximal policy optimization (PPO) is likely to be the best RL method that works with risk-aware reward shaping.