10 citations · 10 across the 13 of their papers we have counts for
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Exploration Behavior of Untrained Policies
Jacob Adamczyk
Exploration remains a fundamental challenge in reinforcement learning (RL), particularly in environments with sparse or adversarial reward structures. In this work, we study how th…
EVAL: EigenVector-based Average-reward Learning
Jacob Adamczyk, Volodymyr Makarenko, Stas Tiomkin +1
In reinforcement learning, two objective functions have been developed extensively in the literature: discounted and averaged rewards. The generalization to an entropy-regularized…
Inferring Transition Dynamics from Value Functions
Jacob Adamczyk
In reinforcement learning, the value function is typically trained to solve the Bellman equation, which connects the current value to future values. This temporal dependency hints…
Average-Reward Soft Actor-Critic
Jacob Adamczyk, Volodymyr Makarenko, Stas Tiomkin +1
The average-reward formulation of reinforcement learning (RL) has drawn increased interest in recent years for its ability to solve temporally-extended problems without relying on…
Bootstrapped Reward Shaping
Jacob Adamczyk, Volodymyr Makarenko, Stas Tiomkin +1
In reinforcement learning, especially in sparse-reward domains, many environment steps are required to observe reward information. In order to increase the frequency of such observ…