The authors build on the insight that existing option discovery algorithms define subgoals as entire states, leading to options that apply only in narrow regions and a combinatorial explosion in the number of options.
Learning options by focusing on relevant features instead of whole states
Bagaria et al. introduce an algorithm that identifies a small subset of relevant features per subgoal, yielding options that generalize broadly and accelerate exploration in sparse-reward environments.
Big Tech
Akhil Bagaria · Anita De Mello Koch · George Konidaris
Amazon · Brown University
Research Digest··3 min read
The authors present an intrinsically motivated option discovery algorithm that learns abstract subgoals by attending only to a relevant subset of state features, rather than requiring the agent to recreate entire states.
Why this paper
From Amazon and Brown University
In one line
Identifying a small relevant subset of features per subgoal yields options that generalize broadly and accelerate exploration.
What we could check
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