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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 381–390 of 514 papers

TitleStatusHype
Efficient exploration of zero-sum stochastic games—0
Particle Filter Based Monocular Human Tracking with a 3D Cardbox Model and a Novel Deterministic Resampling Strategy—0
Misspecification-robust likelihood-free inference in high dimensions—0
Minimax Value Interval for Off-Policy Evaluation and Policy Optimization—0
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal BabblingCode0
Parameterized Indexed Value Function for Efficient Exploration in Reinforcement LearningCode0
Recruitment-imitation Mechanism for Evolutionary Reinforcement Learning—0
Provably Efficient Exploration in Policy Optimization—0
Explicit Planning for Efficient Exploration in Reinforcement Learning—0
Better Exploration with Optimistic Actor CriticCode0
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