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#hierarchical-rl

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papersTODAY 04:00 UTC

Paper proposes agent-controlled goal selection and termination in hierarchical RL

A new arXiv paper examines the agent-centric general value function (ACGVF) approach, which shifts two design choices from the environment or system designer to the learning agent itself. Under this construction, the agent decides both which goal to pursue and when to treat a goal as completed. The note builds on prior work by Tasse et al. (2026) on goal-based hierarchical reinforcement learning.

reinforcement-learning/agents/hierarchical-rl/research