Abstract: Offline reinforcement learning strives to enable agents to effectively utilize pre-collected offline datasets for learning. Such an offline setup tremendously mitigates the problems of ...
POBAX is a reinforcement learning benchmark that tests all forms of partial observability. POBAX has been accepted to RLC 2025. Check out our paper! The benchmark is entirely written in JAX, allowing ...
Abstract: Reinforcement Learning (RL) serves as a fundamental learning paradigm in the field of artificial intelligence, enabling decision-making policies through interactions with environments.
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