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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 411–420 of 514 papers

TitleStatusHype
Efficient Exploration and Discriminative World Model Learning with an Object-Centric Abstraction—0
Efficient Exploration and Value Function Generalization in Deterministic Systems—0
Efficient Exploration for LLMs—0
Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions—0
Efficient Exploration in Binary and Preferential Bayesian Optimization—0
Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path—0
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
Efficient Exploration in Deep Reinforcement Learning: A Novel Bayesian Actor-Critic Algorithm—0
Efficient Exploration in Resource-Restricted Reinforcement Learning—0
Efficient Exploration of Gradient Space for Online Learning to Rank—0
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