Improving Model-Based Reinforcement Learning with Internal State Representations through Self-Supervision
This work addresses sample efficiency and generalization in reinforcement learning for AI agents, though it is incremental as it builds on the MuZero algorithm.
The paper tackles the problem of stabilizing and improving model-based reinforcement learning by adding reconstruction and consistency losses to bind internal state representations to environment states, resulting in significant performance gains in OpenAI Gym environments and enabling self-supervised pretraining for MuZero.
Using a model of the environment, reinforcement learning agents can plan their future moves and achieve superhuman performance in board games like Chess, Shogi, and Go, while remaining relatively sample-efficient. As demonstrated by the MuZero Algorithm, the environment model can even be learned dynamically, generalizing the agent to many more tasks while at the same time achieving state-of-the-art performance. Notably, MuZero uses internal state representations derived from real environment states for its predictions. In this paper, we bind the model's predicted internal state representation to the environment state via two additional terms: a reconstruction model loss and a simpler consistency loss, both of which work independently and unsupervised, acting as constraints to stabilize the learning process. Our experiments show that this new integration of reconstruction model loss and simpler consistency loss provide a significant performance increase in OpenAI Gym environments. Our modifications also enable self-supervised pretraining for MuZero, so the algorithm can learn about environment dynamics before a goal is made available.