10 citations · 10 across the 13 of their papers we have counts for
13 papers
Maximum Entropy Exploration Without the Rollouts
Jacob Adamczyk, Adam Kamoski, Rahul V. Kulkarni
Efficient exploration remains a central challenge in reinforcement learning, serving as a useful pretraining objective for data collection, particularly when an external reward fun…
Thermodynamics of Reinforcement Learning Curricula
Jacob Adamczyk, Juan Sebastian Rojas, Rahul V. Kulkarni
Connections between statistical mechanics and machine learning have repeatedly proven fruitful, providing insight into optimization, generalization, and representation learning. In…
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…
Boosting Soft Q-Learning by Bounding
Jacob Adamczyk, Volodymyr Makarenko, Stas Tiomkin +1
An agent's ability to leverage past experience is critical for efficiently solving new tasks. Prior work has focused on using value function estimates to obtain zero-shot approxima…
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…
Neural Network Degeneration and its Relationship to the Brain
Jacob Adamczyk
This report discusses the application of neural networks (NNs) as small segments of the brain. The networks representing the biological connectome are altered both spatially and te…