Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

Deep learning models are often used with some downstream task. Models solely trained to
achieve accurate predictions may struggle to perform well on the desired downstream tasks …

In offline model-based reinforcement learning (offline MBRL), we learn a dynamic model
from historically collected data, and subsequently utilize the learned model and fixed …

Abstract Model-based reinforcement learning (RL) often achieves higher sample efficiency
in practice than model-free RL by learning a dynamics model to generate samples for policy …

Diffusion-based planning has shown promising results in long-horizon, sparse-reward tasks
by training trajectory diffusion models and conditioning the sampled trajectories using …

We consider the problem of learning models for risk-sensitive reinforcement learning. We
theoretically demonstrate that proper value equivalence, a method of learning models which …

The quintessential model-based reinforcement-learning agent iteratively refines its
estimates or prior beliefs about the true underlying model of the environment. Recent …

Abstract Model-based reinforcement learning agents must make compromises about which
aspects of the environment their models should capture. The value equivalence (VE) …

All biological and artificial agents must act given limits on their ability to acquire and process
information. As such, a general theory of adaptive behavior should be able to account for the …

In model-based reinforcement learning, the conventional approach to addressing world
model bias is to use gradient optimization methods. However, using a singular policy from …