Probabilistic Programming for Model-Based Reinforcement Learning

Abstract

This paper explores the application of probabilistic programming to the field of model-based reinforcement learning (MBRL). Traditional MBRL approaches often rely on deterministic models, which can be brittle and fail to adequately represent the inherent uncertainty in real-world environments. We argue that leveraging probabilistic programming languages allows for the creation of more robust, interpretable, and adaptable RL agents. The core concept involves representing both the environment dynamics and the agent's policy as probabilistic models. This enables the agent to explicitly reason about uncertainty, quantify its confidence in predictions, and ultimately, make more informed decisions. We demonstrate the potential of this approach through a theoretical framework, focusing on the formulation of probabilistic models for state transition and reward functions. The resulting agent can dynamically update its understanding of the environment, leading to improved performance and increased resilience to unforeseen circumstances. This work provides a foundation for future research in probabilistic MBRL and highlights the importance of incorporating uncertainty into the design of intelligent agents.