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World of Bits: An Open-Domain Platform for Web-Based Agents

ICML 2017 · Multi-agent systems and games › Human-AI and goal-conditioned · approach: World-model-based agents · 36 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add supervised behavior cloning to random policyMiniWoB agent · MiniWoB all envssuccess rate: 20.8 -> 24.8Helped
fine-tune the SL policy with RL (A3C)MiniWoB agent · MiniWoB all envssuccess rate: 24.8 -> 34.8Helped
add RL after SLMiniWoB agent · MiniWoB Click envssuccess rate: 35.2 -> 48.5Helped
add RL after SLMiniWoB agent · MiniWoB Drag envssuccess rate: 24.7 -> 34.6Helped
add RL after SLMiniWoB agent · MiniWoB Mouse+Keyboard envssuccess rate: 3.5 -> 7.8Helped
add supervised learning on Mouse+Keyboard tasks vs randomMiniWoB agent · MiniWoB Mouse+Keyboard envssuccess rate: 6.4 -> 3.5Hurt
add SL+RL on Compound tasksMiniWoB agent · MiniWoB Compound envssuccess rate: 3.3 -> 4.3No clear effect
add SL+RLMiniWoB agent · MiniWoB % solved% solved: 12 -> 26Helped
feed previously taken actions as input (ablation)MiniWoB agent · MiniWoBsuccess rateHurt
weigh click and keyboard losses 10 times higher (ablation)SL objective · MiniWoBsuccess rateHelped
replace LocalCNN with GlobalCNN (ablation)FormWoB/QAWoB agent · QAWoBtraining loss: ∼0.2 -> ∼0.08Helped
replace LocalCNN with GlobalCNN (ablation)web agent · QAWoB and FormWoB rewardrewardHelped

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