Temporal abstraction refers to the ability of an agent to use behaviours of controllers which act for a limited, variable amount of...Read More

The ability to discover approximately optimal policies in domains with sparse rewards is crucial to applying reinforcement learning (RL) in many realworld...Read More

Intelligent agents can cope with sensory-rich environments by learning task-agnostic state abstractions. In this paper, we propose mechanisms to approximate causal states,...Read More

Model-based Reinforcement Learning approaches have the promise of being sample efficient. Much of the progress in learning dynamics models in RL has...Read More

Discovering efficient exploration strategies is a central challenge in reinforcement learning (RL), especially in the context of sparse rewards environments. We postulate...Read More

Domain randomization is a popular technique for improving domain transfer, often used in a zero-shot setting when the target domain is unknown...Read More

Reinforcement learning (RL) typically defines a discount factor as part of the Markov Decision Process. The discount factor values future rewards by...Read More

In many finite horizon episodic reinforcement learning (RL) settings, it is desirable to optimize for the undiscounted return – in settings like...Read More

Online, off-policy reinforcement learning algorithms are able to use an experience memory to remember and replay past experiences. In prior work, this...Read More

In this paper we introduce a simple approach for exploration in reinforcement learning (RL) that allows us to develop theoretically justified algorithms...Read More