Optimization
Accelerating Value Iteration with Anchoring
In this paper, we present the first accelerated VI for both the Bellman consistency and optimality operators.
Accelerating Value Iteration with Anchoring Jongmin Lee 1 Ernest K. Ryu
In this paper, we present the first accelerated VI for both the Bellman consistency and optimality operators.
Online Learning under Adversarial Nonlinear Constraints
In many applications, learning systems are required to process continuous non-stationary data streams.
85db52cc08c5e00cfb1d216b1c85ba35-Paper-Conference.pdf
Concretely, we prove that polynomial-sized message-passing GNN's can learn the
Behavior Alignment via Reward Function Optimization Dhawal Gupta University of Massachusetts Y ash Chandak
Designing reward functions for efficiently guiding reinforcement learning (RL) agents toward specific behaviors is a complex task.
Reducing Blackwell and Average Optimality to Discounted MDPs via the Blackwell Discount Factor
We introduce the Blackwell discount factor for Markov Decision Processes (MDPs). Classical objectives for MDPs include discounted, average, and Blackwell opti-mality.