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In Continual Multi-Agent Reinforcement Learning (CMARL), agents learn cooperative policies across sequences of tasks, aiming to adapt effectively to new tasks while preserving the ability to solve previously encountered ones. In many applications, tasks differ in their underlying structure, which can represent, for exa...
Reinforcement learning (RL) for legged robots has recently gained significant attention due to its ability to enable traversal of complex terrains and navigation in cluttered environments. However, most existing approaches focus on static or predictably moving obstacles and rely on handcrafted curricula or manual task...
A unified view of ARD in RL is provided by introducing a taxonomy, organized by the primary driver of the reward variation, that distinguishes external-feedback-driven reward updates from reward adaptations driven by endogenous within-run signals and those conditioned on exogenous context signals.
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