2 papers
cs.AI2025
Deep RL Needs Deep Behavior Analysis: Exploring Implicit Planning by Model-Free Agents in Open-Ended Environments
Riley Simmons-Edler, Ryan P. Badman, Felix Baastad Berg +5
Understanding the behavior of deep reinforcement learning (DRL) agents -particularly as task and agent sophistication increase- requires more than simple comparison of reward curve…
q-bio.NC2025
Discovering alternative solutions beyond the simplicity bias in recurrent neural networks
William Qian, Cengiz Pehlevan
Training recurrent neural networks (RNNs) to perform neuroscience-style tasks has become a popular way to generate hypotheses for how neural circuits in the brain might perform com…