Incremental model-based reinforcement learning with model constraint

In model-based reinforcement learning (RL) approaches, the estimated model of a real environment is learned with limited data and then utilized for policy optimization. As a result, the policy optimization process in model-based RL is influenced by both policy and estimated model updates. In practice, previous model-based RL methods only perform incremental policy constraint to policy updates, which cannot assure the complete incremental updates, thereby limiting the algorithm's performance. To address this issue, we propose an incremental model-based RL update scheme by analyzing the policy optimization procedure of model-based RL. This scheme includes both an incremental model constraint that guarantees incremental updates to the estimated model, and an incremental policy constraint that ensures incremental updates to the policy. Further, we establish a performance bound incorporating the incremental model-based RL update scheme between the real environment and the estimated model, which can assure non-decreasing policy performance improvement in the real environment. To implement the incremental model-based RL update scheme, we develop a simple and efficient model-based RL algorithm known as IMPO (Incremental Model-based Policy Optimization), which leverages previous knowledge to enhance stability during the learning process. Experimental results across various control benchmarks demonstrate that IMPO significantly outperforms previous state-of-the-art model-based RL methods in terms of overall performance and sample efficiency.