The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …

Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

Policy gradient methods are among the most effective methods in challenging reinforcement
learning problems with large state and/or action spaces. However, little is known about even …

Policy gradient (PG) methods are among the most effective methods in challenging
reinforcement learning problems with large state and/or action spaces. However, little is …

We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has
access to an offline dataset and the ability to collect experience via real-world online …

While policy-based reinforcement learning (RL) achieves tremendous successes in practice,
it is significantly less understood in theory, especially compared with value-based RL. In …

We examine global non-asymptotic convergence properties of policy gradient methods for
multi-agent reinforcement learning (RL) problems in Markov potential games (MPGs). To …

We present foundation language models developed to power Apple Intelligence features,
including a~ 3 billion parameter model designed to run efficiently on devices and a large …

Direct policy gradient methods for reinforcement learning are a successful approach for a
variety of reasons: they are model free, they directly optimize the performance metric of …

Preventive maintenance and production scheduling are two important and interactive
activities in production systems. In this work, the integrated optimization problem of …