Robotics, Autonomous Intelligence and Learning Lab
The RAIL Lab, established in 2014, is dedicated to conducting cutting-edge research in the field of artificially intelligent systems. With a focus on both fundamental and applied research, our vision is to serve as a prominent centre of excellence and a hub for AI activities in Africa. We aim to make significant contributions to the field of AI while also applying our findings to benefit society at large.
Latest Research
Composition and Zero-Shot Transfer with Lattice Structures in Reinforcement Learning
An important property of long-lived agents is the ability to reuse existing knowledge to solve new tasks. An appealing approach towards …
GameTraversalBenchmark: Evaluating Planning Abilities Of Large Language Models Through Traversing 2D Game Maps
Large language models (LLMs) have recently demonstrated great success in generating and understanding natural language. While they have …
Compositional Instruction Following with Language Models and Reinforcement Learning
Combining reinforcement learning with language grounding is challenging as the agent needs to explore the environment while …
The Zeno’s Paradox of ‘Low-Resource’ Languages
The disparity in the languages commonly studied in Natural Language Processing (NLP) is typically reflected by referring to languages …
Optimal Task Generalisation in Cooperative Multi-Agent Reinforcement Learning
While task generalisation is widely studied in the context of single-agent reinforcement learning (RL), little research exists in the …
ROSARL: Reward-Only Safe Reinforcement Learning
An important problem in reinforcement learning is designing agents that learn to solve tasks safely in an environment. A common …