5 papers · 1 filter
Variance-Reduced Q-Learning over Static and Time-Varying Networks
Sreejeet Maity, Feng Zhu, Aritra Mitra +1
We investigate a decentralized reinforcement learning problem involving multiple agents that interact with the same Markov Decision Process (MDP). The agents can exchange informati…
Lifelong In-Context Learning with Transformers Requires Parametric Forms of Attention
Luke McDermott, Robert W. Heath, Rahul Parhi
Lifelong continual learning remains an obstacle on the path to human-like intelligence. Modern transformers show sparks of intelligence with in-context learning. The quadratic natu…
A Short and Unified Convergence Analysis of the SAG, SAGA, and IAG Algorithms
Feng Zhu, Robert W. Heath, Aritra Mitra
Stochastic variance-reduced algorithms such as Stochastic Average Gradient (SAG) and SAGA, and their deterministic counterparts like the Incremental Aggregated Gradient (IAG) metho…
Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling
Feng Zhu, Aritra Mitra, Robert W. Heath
Motivated by collaborative reinforcement learning (RL) and optimization with time-correlated data, we study a generic federated stochastic approximation problem involving agent…
Towards Fast Rates for Federated and Multi-Task Reinforcement Learning
Feng Zhu, Robert W. Heath, Aritra Mitra
We consider a setting involving agents, where each agent interacts with an environment modeled as a Markov Decision Process (MDP). The agents' MDPs differ in their reward funct…