Hybrid Action Reinforcement Learning

Hybrid action reinforcement learning (HARL) addresses the challenge of optimizing systems with both continuous and discrete control variables, a common feature in many real-world problems. Current research focuses on developing HARL algorithms, often employing actor-critic methods or variations of Q-learning, that effectively handle this mixed action space, sometimes incorporating techniques like action decoders to translate latent continuous actions into discrete choices. These methods are proving valuable in diverse applications, including drone control, energy management in hybrid vehicles, and algorithmic trading, where efficient and near-optimal control of complex systems is crucial.