This chapter studies the bidirectional causal interactions between curiosity and learning and
discusses how understanding these interactions can be leveraged in educational …

Intelligent animals devote much time and energy to exploring and obtaining information, but
the underlying mechanisms are poorly understood. We review recent developments on this …

Reinforcement learning allows solving complex tasks, however, the learning tends to be task-
specific and the sample efficiency remains a challenge. We present Plan2Explore, a self …

We introduce an exploration bonus for deep reinforcement learning methods that is easy to
implement and adds minimal overhead to the computation performed. The bonus is the error …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

Reinforcement learning algorithms rely on carefully engineering environment rewards that
are extrinsic to the agent. However, annotating each environment with hand-designed …

In many real-world scenarios, rewards extrinsic to the agent are extremely sparse, or absent
altogether. In such cases, curiosity can serve as an intrinsic reward signal to enable the …

Efficient exploration is a long-standing problem in sensorimotor learning. Major advances
have been demonstrated in noise-free, non-stochastic domains such as video games and …

We consider an agent's uncertainty about its environment and the problem of generalizing
this uncertainty across states. Specifically, we focus on the problem of exploration in non …

Abstract Bellemare et al.(2016) introduced the notion of a pseudo-count, derived from a
density model, to generalize count-based exploration to non-tabular reinforcement learning …