In this work, we consider the regret minimization problem for reinforcement learning in latent
Markov Decision Processes (LMDP). In an LMDP, an MDP is randomly drawn from a set of …

We study the problem of learning a mixture of multiple linear dynamical systems (LDSs) from
unlabeled short sample trajectories, each generated by one of the LDS models. Despite the …

We study multi-task reinforcement learning (RL) in tabular episodic Markov decision
processes (MDPs). We formulate a heterogeneous multi-player RL problem, in which a …

We consider episodic reinforcement learning in reward-mixing Markov decision processes
(RMMDPs): at the beginning of every episode nature randomly picks a latent reward model …

We are interested in how to design reinforcement learning agents that provably reduce the
sample complexity for learning new tasks by transferring knowledge from previously-solved …

Transferring knowledge among various environments is important for efficiently learning
multiple tasks online. Most existing methods directly use the previously learned models or …

We study regret minimization for reinforcement learning (RL) in Latent Markov Decision
Processes (LMDPs) with context in hindsight. We design a novel model-based algorithmic …

We study computational and statistical aspects of learning Latent Markov Decision
Processes (LMDPs). In this model, the learner interacts with an MDP drawn at the beginning …

Informed and robust decision making in the face of uncertainty is critical for robots operating
in unstructured environments. We formulate this as Bayesian Reinforcement Learning over …

Statistical learning with missing or hidden information is ubiquitous in many practical
problems. For example, the success of a certain medical treatment can largely depend on …