One of the key capabilities of intelligent agents is the ability to discover useful skills without
external supervision. However, the current unsupervised skill discovery methods are often …

Unsupervised pre-training strategies have proven to be highly effective in natural language
processing and computer vision. Likewise, unsupervised reinforcement learning (RL) holds …

Designing generalizable agents capable of adapting to diverse embodiments has achieved
significant attention in Reinforcement Learning (RL), which is critical for deploying RL …

This paper investigates two fundamental problems that arise when utilizing Intrinsic
Motivation (IM) for reinforcement learning in Reward-Free Pre-Training (RFPT) tasks and …

Humans possess the ability to draw on past experiences explicitly when learning new tasks
and applying them accordingly. We believe this capacity for self-referencing is especially …

The growing number of complex and heterogeneous nodes and base station applications
has required a high computational complexity to handle wireless resources. To tackle this …

In unsupervised reinforcement learning, agents traverse a reward-free environment, aiming
for rapid generalisation to subsequent tasks. This strategy offers a compelling resolution to …

Both entropy-minimizing and entropy-maximizing (curiosity) objectives for unsupervised
reinforcement learning (RL) have been shown to be effective in different environments …

This paper investigates two fundamental problems that arise when implementing intrinsic
motivation for reinforcement learning: 1) how to design a proper intrinsic objective for …

Unsupervised reinforcement learning (URL) has been a promising paradigm in recent years
to design reinforcement learning (RL) agents that can be generalized to new tasks …