4 papers · 1 filter
From POMDP Theory to Deep RL with Particle Filters
Kevin Tan, Ziping Xu
Model-based reinforcement learning in partially observable environments requires inferring hidden states, learning latent dynamics, and planning under the resulting belief. Practic…
Improving scalable oversight with co-trained monitors
Joseph H. Rudoler, Kevin Tan, Benedict Tessler +2
Worker-monitor setups are a promising approach to AI oversight, but training workers against fixed monitors can incentivize monitor evasion. We study whether this failure mode can…
Estimating Implicit Regularization in Deep Learning
Joseph H. Rudoler, Kevin Tan, Giles Hooker +1
Deep learning systems are known to exhibit implicit regularization (alt. implicit bias), favoring simple solutions instead of merely minimizing the loss function. In some cases, we…
Optimistic Actor-Critic with Parametric Policies for Linear Markov Decision Processes
Max Qiushi Lin, Reza Asad, Kevin Tan +3
Although actor-critic methods have been successful in practice, their theoretical analyses have several limitations. Specifically, existing theoretical work either sidesteps the ex…