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Efficient Exploration

Efficient Exploration is one of the main obstacles in scaling up modern deep reinforcement learning algorithms. The main challenge in Efficient Exploration is the balance between exploiting current estimates, and gaining information about poorly understood states and actions.

Source: Randomized Value Functions via Multiplicative Normalizing Flows

Papers

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Showing 431–440 of 514 papers

TitleStatusHype
Efficient gPC-based quantification of probabilistic robustness for systems in neuroscience—0
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation—0
Efficient Policy Space Response Oracles—0
Efficient Pose and Cell Segmentation using Column Generation—0
Reinforcement Learning for Causal Discovery without Acyclicity Constraints—0
Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization—0
Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling—0
Embodied Agents for Efficient Exploration and Smart Scene Description—0
Emotion-Agent: Unsupervised Deep Reinforcement Learning with Distribution-Prototype Reward for Continuous Emotional EEG Analysis—0
End-Effect Exploration Drive for Effective Motor Learning—0
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