Bridging Physics-Informed Neural Networks with Reinforcement Learning: Hamilton-Jacobi-Bellman Proximal Policy Optimization (HJBPPO)
This work addresses the challenge of training value networks in reinforcement learning for researchers and practitioners, though it appears incremental as it builds on existing PPO methods.
The paper tackled the problem of improving reinforcement learning in continuous spaces by combining the Hamilton-Jacobi-Bellman equation with Proximal Policy Optimization, resulting in the HJBPPO algorithm that showed improved performance over PPO on MuJoCo environments.
This paper introduces the Hamilton-Jacobi-Bellman Proximal Policy Optimization (HJBPPO) algorithm into reinforcement learning. The Hamilton-Jacobi-Bellman (HJB) equation is used in control theory to evaluate the optimality of the value function. Our work combines the HJB equation with reinforcement learning in continuous state and action spaces to improve the training of the value network. We treat the value network as a Physics-Informed Neural Network (PINN) to solve for the HJB equation by computing its derivatives with respect to its inputs exactly. The Proximal Policy Optimization (PPO)-Clipped algorithm is improvised with this implementation as it uses a value network to compute the objective function for its policy network. The HJBPPO algorithm shows an improved performance compared to PPO on the MuJoCo environments.