Dmitrii Dobriborsci

Effects of Sampling and Prediction Horizon in Reinforcement Learning

2021 · IEEE Access

Plain reinforcement learning (RL) may be prone to loss of convergence, constraint violation, unexpected performance, etc. Commonly, RL agents undergo extensive learning stages to achieve acceptable functionality. This is in contrast to classical control algorithms …