We examine the question of when and how parametric models are most useful in
reinforcement learning. In particular, we look at commonalities and differences between …

We consider off-policy policy evaluation with function approximation (FA) in average-reward
MDPs, where the goal is to estimate both the reward rate and the differential value function …

To achieve the ambitious goals of artificial intelligence, reinforcement learning must include
planning with a model of the world that is abstract in state and time. Deep learning has made …

We address the problem of credit assignment in reinforcement learning and explore
fundamental questions regarding the way in which an agent can best use additional …

In this paper we investigate the properties of representations learned by deep reinforcement
learning systems. Much of the early work on representations for reinforcement learning …

We present a new approach for efficient exploration which leverages a low-dimensional
encoding of the environment learned with a combination of model-based and model-free …

In recent years, a growing number of deep model-based reinforcement learning (RL)
methods have been introduced. The interest in deep model-based RL is not surprising …

Planning for multi-robot coordination during long horizon missions in complex environments
need to consider resources, temporal constraints, and uncertainty. This could be …

The optimal policy of a reinforcement learning problem is often discontinuous and non-
smooth. Ie, for two states with similar representations, their optimal policies can be …

In model-based reinforcement learning, simulated experiences from the learned model are
often treated as equivalent to experience from the real environment. However, when the …