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 …

A key challenge in training generally-capable agents is the design of training tasks that
facilitate broad generalization and robustness to environment variations. This challenge …

Curriculum learning is a training mechanism in reinforcement learning (RL) that facilitates
the achievement of complex policies by progressively increasing the task difficulty during …

A wide range of real-world applications can be formulated as Multi-Agent Path Finding
(MAPF) problem, where the goal is to find collision-free paths for multiple agents with …

This study introduces an innovative framework that employs large language models (LLMs)
to automate the design and generation of curricula for reinforcement learning (RL). As …

A long-running challenge in the reinforcement learning (RL) community has been to train a
goal-conditioned agent in sparse reward environment such that it also generalizes to …

Robust reinforcement learning (RL) considers the problem of learning policies that perform
well in the worst case among a set of possible environment parameter values. In real-world …

This paper presents a novel exploration of the use of an evolving neural network approach
to generate dynamic content for video games, specifically for a tower defence game. The …

Recent work on designing an appropriate distribution of environments has shown promise
for training effective generally capable agents. Its success is partly because of a form of …

Advancements in reinforcement learning (RL) have demonstrated superhuman performance
in complex tasks such as Starcraft, Go, Chess etc. However, knowledge transfer from …