Brett Daley

Adaptive Tree Backup Algorithms for Temporal-Difference Reinforcement Learning

2022 · arXiv (Cornell University)

Q($σ$) is a recently proposed temporal-difference learning method that interpolates between learning from expected backups and sampled backups. It has been shown that intermediate values for the interpolation parameter $σ\in [0,1]$ perform better in practice, …

Deep Reinforcement Learning with Gradient Eligibility Traces

2025 · arXiv (Cornell University)

Achieving fast and stable off-policy learning in deep reinforcement learning (RL) is challenging. Most existing methods rely on semi-gradient temporal-difference (TD) methods for their simplicity and efficiency, but are consequently susceptible to divergence. While more …