3 papers
cs.LG2026
Representation Learning Enables Scalable Multitask Deep Reinforcement Learning
Johan Obando-Ceron, Lu Li, Scott Fujimoto +3
Scaling reinforcement learning (RL) to diverse multitask settings remains a central challenge. While recent advances in model-based RL achieve strong performance, they rely on plan…
cs.AI2025
Meta-World+: An Improved, Standardized, RL Benchmark
Reginald McLean, Evangelos Chatzaroulas, Luc McCutcheon +9
Meta-World is widely used for evaluating multi-task and meta-reinforcement learning agents, which are challenged to master diverse skills simultaneously. Since its introduction how…
cs.LG2025
Multi-Task Reinforcement Learning Enables Parameter Scaling
Reginald McLean, Evangelos Chatzaroulas, Jordan Terry +3
Multi-task reinforcement learning (MTRL) aims to endow a single agent with the ability to perform well on multiple tasks. Recent works have focused on developing novel sophisticate…