Gradient Q$(σ, λ)$: A Unified Algorithm with Function Approximation for Reinforcement Learning

6 Sep 2019Long YangYu ZhangQian ZhengPengfei LiGang Pan

Full-sampling (e.g., Q-learning) and pure-expectation (e.g., Expected Sarsa) algorithms are efficient and frequently used techniques in reinforcement learning. Q$(\sigma,\lambda)$ is the first approach unifies them with eligibility trace through the sampling degree $\sigma$... (read more)

PDF Abstract


No code implementations yet. Submit your code now

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.