239 citations · 996 across the 29 of their papers we have counts for
4 papers · 1 filter
Bounding the Optimal Value Function in Compositional Reinforcement Learning
Jacob Adamczyk, Volodymyr Makarenko, Argenis Arriojas +2
In the field of reinforcement learning (RL), agents are often tasked with solving a variety of problems differing only in their reward functions. In order to quickly obtain solutio…
Leveraging Prior Knowledge in Reinforcement Learning via Double-Sided Bounds on the Value Function
Jacob Adamczyk, Stas Tiomkin, Rahul Kulkarni
An agent's ability to leverage past experience is critical for efficiently solving new tasks. Approximate solutions for new tasks can be obtained from previously derived value func…
Utilizing Prior Solutions for Reward Shaping and Composition in Entropy-Regularized Reinforcement Learning
Jacob Adamczyk, Argenis Arriojas, Stas Tiomkin +1
In reinforcement learning (RL), the ability to utilize prior knowledge from previously solved tasks can allow agents to quickly solve new problems. In some cases, these new problem…
Entropy Regularized Reinforcement Learning Using Large Deviation Theory
Argenis Arriojas, Jacob Adamczyk, Stas Tiomkin +1
Reinforcement learning (RL) is an important field of research in machine learning that is increasingly being applied to complex optimization problems in physics. In parallel, conce…