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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 121–130 of 514 papers

TitleStatusHype
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration—0
KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance—0
Discovering Context Specific Causal Relationships—0
BooVI: Provably Efficient Bootstrapped Value Iteration—0
Distilling Realizable Students from Unrealizable Teachers—0
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning—0
Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation—0
Efficient Exploration of Gradient Space for Online Learning to Rank—0
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning—0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
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