3260 papers • 126 benchmarks • 313 datasets
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These leaderboards are used to track progress in active-observation-completion-5
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Use these libraries to find active-observation-completion-5 models and implementations
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This work proposes a reinforcement learning solution, where the agent is rewarded for reducing its uncertainty about the unobserved portions of its environment, and introduces sidekick policy learning, which exploits the asymmetry in observability between training and test time.
Adding a benchmark result helps the community track progress.