1 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
Actor-Accelerated Policy Dual Averaging for Reinforcement Learning in Continuous Action Spaces
Ji Gao, Caleb Ju, Guanghui Lan +1
Policy Dual Averaging (PDA) offers a principled Policy Mirror Descent (PMD) framework that more naturally admits value function approximation than standard PMD, enabling the use of…
Auto-exploration for online reinforcement learning
Caleb Ju, Guanghui Lan
The exploration-exploitation dilemma in reinforcement learning (RL) is a fundamental challenge to efficient RL algorithms. Existing algorithms for finite state and action discounte…
Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration
Caleb Ju, Constance Crozier
Variable renewable generation increases the challenge of balancing power supply and demand. Grid-scale batteries co-located with generation can help mitigate this misalignment. Thi…
Strongly-polynomial time and validation analysis of policy gradient methods
Caleb Ju, Guanghui Lan
This paper proposes a novel termination criterion, termed the advantage gap function, for finite state and action Markov decision processes (MDP) and reinforcement learning (RL). B…
Policy Optimization over General State and Action Spaces
Caleb Ju, Guanghui Lan
Reinforcement learning (RL) problems over general state and action spaces are notoriously challenging. In contrast to the tableau setting, one can not enumerate all the states and…