Reinforcement Learning
Large Language Models Can Implement Policy Iteration Ethan Brooks 1, Logan Walls 2, Richard L. Lewis
Gradient techniques are inherently slow, sacrificing the "few-shot" quality
Overleaf Example
Second, we enforce the posterior update model to learn the dynamics of the latent variable.
An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learning
In the standard reinforcement learning (RL) setting, the primary goal is to obtain a policy that maximizes a cumulative scalar reward [Sutton and Barto, 2018].
Safety through feedback in Constrained RL
This feedback can be system generated or elicited from a human observing the training process. Previous approaches have not been able to scale to complex environments and are constrained to receiving feedback at the state level which can be expensive to collect. To this end, we introduce an approach that scales to more complex domains and extends beyond state-level feedback, thus, reducing the burden on the evaluator.
Dispelling the Mirage of Progress in Offline MARL through Standardised Baselines and Evaluation
This paper proceeds as follows.
Scalable and Effective Arithmetic Tree Generation for Adder and Multiplier Designs
Across a wide range of hardware scenarios, the computational efficiency and physical size of the arithmetic units significantly influence the speed and footprint of the overall hardware system.
GUIDE: Real-Time Human-Shaped Agents
Due to their inherent complexity, these tasks pose significant challenges for current machine learning systems.
Off-Policy Selection for Initiating Human-Centric Experimental Design Ge Gao Xi Y ang
Human-centric systems (HCSs), e.g. , used in healthcare facilities [ Given the long testing horizon ( e.g. , several years, or semesters, in healthcare, and IE, respectively) and the high cost of recruiting participants, online testing is considered exceedingly The work was done at North Carolina State University. In this section, we introduce the FPS method, which determines the policy to be deployed to new participants that join an existing cohort, conditioned only on their initial states.