A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
TBQ(σ) improves trace utilization in off-policy reinforcement learning.
The paper analyzes off-policy TD-learning using generalized Bellman operators and provides finite-sample bounds.
This study evaluates multi-step reinforcement learning methods in the Mountain Car environment.
Recently, a new multi-step temporal learning algorithm, called , unifies -step Tree-Backup (when ) and -step Sarsa (when ) by introducing a sampling parameter . However, similar to other multi-step temporal-difference learning algorithms, needs much memory consumption and computation tim…
Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori. However, in complex environments where transition models need t…
Reinforcement learning methods carry a well known bias-variance trade-off in n-step algorithms for optimal control. Unfortunately, this has rarely been addressed in current research. This trade-off principle holds independent of the choice of the algorithm, such as n-step SARSA, n-step Expected SARSA or n-step Tree bac…