Multi-Step Reinforcement Learning: A Unifying Algorithm
Multi-Step Reinforcement Learning: A Unifying Algorithm
Unifying seemingly disparate algorithmic ideas to produce better performing algorithms has been a longstanding goal in reinforcement learning. As a primary example, TD(λ) elegantly unifies one-step TD prediction with Monte Carlo methods through the use of eligibility traces and the trace-decay parameter. Currently, there are a multitude of algorithms that …