This paper reviews exploration techniques in deep reinforcement learning. Exploration
techniques are of primary importance when solving sparse reward problems. In sparse …

Thompson sampling is an algorithm for online decision problems where actions are taken
sequentially in a manner that must balance between exploiting what is known to maximize …

We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered
robotics. BEHAVIOR-1K includes two components, guided and motivated by the results of an …

Centralised training with decentralised execution is an important setting for cooperative
deep multi-agent reinforcement learning due to communication constraints during execution …

This paper studies model-based reinforcement learning (RL) for regret minimization. We
focus on finite-horizon episodic RL where the transition model $ P $ belongs to a known …

Strength exercise is a strategy applied in sports and physical training processes. It may
induce skeletal muscle hypertrophy. The hypertrophy is dependent on the eccentric muscle …

Exploration in reinforcement learning (RL) suffers from the curse of dimensionality when the
state-action space is large. A common practice is to parameterize the high-dimensional …

Dealing with uncertainty is essential for efficient reinforcement learning. There is a growing
literature on uncertainty estimation for deep learning from fixed datasets, but many of the …

Q-learning, originally an incremental algorithm for estimating an optimal decision strategy in
an infinite-horizon decision problem, now refers to a general class of reinforcement learning …

Intelligence relies on an agent's knowledge of what it does not know. This capability can be
assessed based on the quality of joint predictions of labels across multiple inputs. In …