1 citations · 2 across the 6 of their papers we have counts for
7 papers
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…
Preconditioning via Randomized Range Deflation (RandRAND)
Oleg Balabanov, Caleb Ju, Kaiwen He +2
We introduce RandRAND, a new class of randomized preconditioning methods for large-scale linear systems. RandRAND deflates the spectrum via efficient orthogonal projections onto ra…
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…
A model-free first-order method for linear quadratic regulator with sampling complexity
Caleb Ju, Georgios Kotsalis, Guanghui Lan
We consider the classic stochastic linear quadratic regulator (LQR) problem under an infinite horizon average stage cost. By leveraging recent policy gradient methods from reinforc…
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…