John D. Martin

Adapting the Function Approximation Architecture in Online Reinforcement Learning

2021 · arXiv (Cornell University)

The performance of a reinforcement learning (RL) system depends on the computational architecture used to approximate a value function. Deep learning methods provide both optimization techniques and architectures for approximating nonlinear functions from noisy, high-dimensional …

Should Models Be Accurate?

2022 · arXiv (Cornell University)

Model-based Reinforcement Learning (MBRL) holds promise for data-efficiency by planning with model-generated experience in addition to learning with experience from the environment. However, in complex or changing environments, models in MBRL will inevitably be imperfect, …

On the Interplay Between Sparsity and Training in Deep Reinforcement Learning

2025 · arXiv (Cornell University)

We study the benefits of different sparse architectures for deep reinforcement learning. In particular, we focus on image-based domains where spatially-biased and fully-connected architectures are common. Using these and several other architectures of equal capacity, …