Provable Causal State Representation under Asynchronous Diffusion Model for POMDPs
Keywords: diffusion model, causal state representation, model uncertainty, bisimulaion, POMDP
TL;DR: We present a new RL framework CSR-ADM, which can accommodate and enhance any RL algorithm by capturing causal state representations for POMDPs under perturbation via a novel asynchronous diffusion model and bisimulation technology.
Abstract: A major challenge in applying reinforcement learning (RL) to real-world scenarios is managing high-dimensional, noisy perception input signals. Identifying and utilizing representations that contain sufficient and essential information for decision-making tasks is key to computational efficiency and generalization of RL by reducing bias in decision-making processes. In this paper, we present a new RL framework, named *Causal State Representation under Asynchronous Diffusion Model (CSR-ADM)*, which accommodates and enhances any RL algorithm for partially observable Markov decision processes (POMDPs) with perturbed inputs. A new asynchronous diffusion model is proposed to denoise both reward and observation spaces, and integrated with the bisimulation technology to capture causal state representations in POMDPs. Notably, the causal state is the coarsest partition of the denoised observations. We link the causal state to a causal feature set and provide theoretical guarantees by deriving the upper bound on value function approximation between the noisy observation space and the causal state space, demonstrating equivalence to bisimulation under the Lipschitz assumption. To the best of our knowledge, CSR-ADM is the first framework to approximate causal states with diffusion models, substantiated by a comprehensive theoretical foundation. Extensive experiments on Roboschool tasks show that CSR-ADM outperforms state-of-the-art methods, significantly improving the robustness of existing RL algorithms under varying scales of random noise.
Primary Area: reinforcement learning
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Submission Number: 8638
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