The world contains a lot of information that’s irrelevant for you to be able to navigate it. At the core of many Machine Learning techniques is the ability to discern relevant and useful signals — or patterns — from noise..
This work formalizes the problem of separating “the good from the irrelevant information” in the context of reinforcement learning by identifying information that’s both controllable by the agent and relevant for the reward, as described in the figure below.
Based on this idea, the authors propose Denoised MDPs (Markov Decision Process), a method for learning a factorization of state representations that disentangles the controllable and reward-relevant bit of the state using information theoretic principles. The gist of it is that different factors of the state should be maximally or minimally predictive of other factors depending on their relationship, which enables the authors to set up a variational objective for the agent to optimize.
The result is a world model that explicitly models what information should be discarded as noise and what information should be used for modelling the agent’s decisions. The authors prove how this approach is competitive in the DeepMind Control Suite, and fascinatingly, they showcase qualitatively how the Denoised MDP representations work by training a decoder on reconstructing the input so you can understand what the signal representation of the state learns to capture.
The ability to separate signal from noise, and reason with clean abstractions, is critical to intelligence. With this ability, humans can efficiently perform real world tasks without considering all possible nuisance factors. How can artificial agents do the same? What kind of information can agents safely discard as noises? In this work, we categorize information out in the wild into four types based on controllability and relation with reward, and formulate useful information as that which is both controllable and reward-relevant.
This framework clarifies the kinds of information removed by various prior work on representation learning in reinforcement learning (RL), and leads to our proposed approach of learning a Denoised MDP that explicitly factors out certain noise distractors. Extensive experiments on variants of DeepMind Control Suite and RoboDesk demonstrate superior performance of our denoised world model over using raw observations alone, and over prior works, across policy optimization control tasks as well as the non-control task of joint position regression.
Link to the project: https://www.tongzhouwang.info/denoised_mdp/
Tongzhou Wang, Simon S. Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian. “Denoised MDPs: Learning World Models Better Than The World Itself” International Conference on Machine Learning (ICML). 2022.