Neural ODE Regularization Boosts Reinforcement Learning Performance.

In sequential decision-making tasks, neural networks often rely on latent representations of environmental states. While the environment's actual dynamics dictate how semantic states evolve, the corresponding transitions in the latent space are typically implicit, potentially leading to a mismatch. This research proposes a novel regularization technique that explicitly models these latent dynamics. The method draws an analogy between Markov Decision Process (MDP) trajectories and Ordinary Differential Equation (ODE) flows, where the current state fully determines its successors. By enforcing latent embeddings to follow consistent ODE flows, the technique aligns the representation learning process with the environment's true dynamics. Although broadly applicable, the effectiveness of this neural ODE-based regularization is demonstrated within Actor-Critic reinforcement learning algorithms. It yields substantial performance gains across standard Atari benchmarks for A2C and in gridworld environments for PPO, indicating a significant step forward in improving the stability and efficiency of deep learning agents.