An optimistic confidence-interval ranking procedure for best-arm identification across multiple independent bandits yields lower average simple regret and error probability than prior methods when selecting high-performing agents for each game in GVGAI and Ludii.
ViZDoom: A Doom-based AI research platform for visual reinforcement learning
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CAMAL adds an auxiliary regularizer during training that aligns model attention with segmentation masks to improve both spatial accuracy and causal faithfulness of attention in deep learning and deep reinforcement learning vision models.
Empirical tests in VizDoom show multiple DQN updates per step do not improve performance after learning rate adjustment, with a 4:1 update-to-step ratio optimal before significant degradation.
citing papers explorer
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Best Agent Identification for General Game Playing
An optimistic confidence-interval ranking procedure for best-arm identification across multiple independent bandits yields lower average simple regret and error probability than prior methods when selecting high-performing agents for each game in GVGAI and Ludii.
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CAMAL: Improving Attention Alignment and Faithfulness with Segmentation Masks
CAMAL adds an auxiliary regularizer during training that aligns model attention with segmentation masks to improve both spatial accuracy and causal faithfulness of attention in deep learning and deep reinforcement learning vision models.
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Optimal Use of Experience in First Person Shooter Environments
Empirical tests in VizDoom show multiple DQN updates per step do not improve performance after learning rate adjustment, with a 4:1 update-to-step ratio optimal before significant degradation.