Model predictive control-based value estimation for efficient reinforcement learning
This addresses efficiency issues in reinforcement learning for applications like robotics, but it appears incremental as it builds on existing model-based approaches.
The paper tackles the problem of reinforcement learning requiring many environment interactions by proposing a model predictive control-based method that improves learning efficiency, convergence speed, and reduces sample buffer requirements, with validation in classic databases and a UAV obstacle avoidance scenario.
Reinforcement learning suffers from limitations in real practices primarily due to the number of required interactions with virtual environments. It results in a challenging problem because we are implausible to obtain a local optimal strategy with only a few attempts for many learning methods. Hereby, we design an improved reinforcement learning method based on model predictive control that models the environment through a data-driven approach. Based on the learned environment model, it performs multi-step prediction to estimate the value function and optimize the policy. The method demonstrates higher learning efficiency, faster convergent speed of strategies tending to the local optimal value, and less sample capacity space required by experience replay buffers. Experimental results, both in classic databases and in a dynamic obstacle avoidance scenario for an unmanned aerial vehicle, validate the proposed approaches.