FF-Replan was the winner of the 2004 International Probabilistic Planning Competition (IPPC-04)(Younes
& Littman 2004a) and was also the top performer on IPPC-06 domains, …

While deep reinforcement learning (deep RL) agents are effective at maximizing rewards, it
is often unclear what strategies they use to do so. In this paper, we take a step toward …

We consider the problem of multi-task reinforcement learning, where the agent needs to
solve a sequence of Markov Decision Processes (MDPs) chosen randomly from a fixed but …

Accurate and precise terrain estimation is a difficult problem for robot locomotion in real-world
environments. Thus, it is useful to have systems that do not depend on accurate estimation …

This work presents a discriminative training method for particle filters in the context of multi-object
tracking. We are motivated by the difficulty of hand-tuning the many model parameters …

There is a growing interest in intelligent assistants for a variety of applications from sorting
email to helping people with disabilities to do their daily chores. In this paper, we formulate the …

We explore approximate policy iteration, replacing the usual costfunction learning step with
a learning step in policy space. We give policy-language biases that enable solution of very …

We study reward decomposition for explaining the decisions of reinforcement learning (RL)
agents. The approach decomposes rewards into sums of semantically meaningful reward …

We consider the problem of learning control policies via trajectory preference queries to an
expert. In particular, the learning agent can present an expert with short runs of a pair of …

We apply fast online trajectory optimization for multi-step motion planning to Cassie, a
bipedal robot designed to exploit natural spring-mass locomotion dynamics using lightweight, …