Offline reinforcement learning enables agents to leverage large pre-collected datasets of
environment transitions to learn control policies, circumventing the need for potentially …

Designing and analyzing model-based RL (MBRL) algorithms with guaranteed monotonic
improvement has been challenging, mainly due to the interdependence between policy …

We propose a novel approach to addressing two fundamental challenges in Model-based
Reinforcement Learning (MBRL): the computational expense of repeatedly finding a good …

Abstract ReParameterization (RP) Policy Gradient Methods (PGMs) have been widely
adopted for continuous control tasks in robotics and computer graphics. However, recent …

We consider the problem of quantifying uncertainty over expected cumulative rewards in
model-based reinforcement learning. In particular, we focus on characterizing the variance …

In many practical applications of RL, it is expensive to observe state transitions from the
environment. For example, in the problem of plasma control for nuclear fusion, computing …

Model-based reinforcement learning (MBRL) is a sample efficient technique to obtain control
policies, yet unavoidable modeling errors often lead performance deterioration. The model …

Many potential applications of reinforcement learning (RL) are stymied by the large numbers
of samples required to learn an effective policy. This is especially true when applying RL to …

Object pushing presents a key non-prehensile manipulation problem that is illustrative of
more complex robotic manipulation tasks. While deep reinforcement learning (RL) methods …

Monte Carlo methods have become increasingly relevant for control of non-differentiable
systems, approximate dynamics models, and learning from data. These methods scale to …