Practical algorithm bridges theory and deep reinforcement learning in hidden state problems
From POMDP Theory to Deep RL with Particle Filters
Machine Learning
Summary
Many problems in artificial intelligence involve making decisions without knowing all the facts, which makes learning and planning difficult. The authors propose a new practical method that helps computers guess hidden information, learn how things change, and plan actions better using a mix of smart guessing and deep learning. They also prove how different parts of the method affect overall performance and show that their approach works well in tests. This helps connect complex theoretical ideas to real-world learning algorithms.
What this means in practice
- •For robotics engineers: Improve robot decision making in uncertain environments by better estimating hidden states and planning steps ahead using the proposed algorithm.
- •For autonomous vehicle software teams: Enhance vehicle control systems that deal with incomplete sensor data by integrating the particle filter and deep learning method for online planning.
Authors
Kevin Tan, Ziping Xu
Abstract
Model-based reinforcement learning in partially observable environments requires inferring hidden states, learning latent dynamics, and planning under the resulting belief. Practical algorithms often solve one aspect of the above pipeline while relying on approximations for the others that are often poorly understood, while existing POMDP theory typically analyzes computationally inefficient theoretical algorithms far from modern deep RL practice. To fill this gap, we propose a practical end-to-end algorithm that approximately solves the hidden state filtering problem via sequential Monte Carlo (SMC), updates a latent dynamics model online, and performs approximate online planning for $d$ steps before bootstrapping leaves with a state-value critic (a QMDP approximation). We analyze each approximation and prove an end-to-end regret bound decomposing the error into model estimation error, particle filter error, Monte Carlo rollout error, and a structural bias term induced by the QMDP approximation controllable by the planning depth that we further analyze in various special cases. The resulting guarantee makes explicit how statistical sample size, particle budget, rollout budget, and planning depth affect performance. We instantiate our algorithm in an MPC-based deep RL framework, demonstrating its practicality on a variety of environments. On a POMDP that satisfies our assumptions, each term of the bound decays at its predicted rate. Our results provide a practical and theoretically grounded route for bridging theory and practice within online decision-making in POMDPs.