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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 161–170 of 514 papers

TitleStatusHype
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards—0
Efficient gPC-based quantification of probabilistic robustness for systems in neuroscience—0
Context-Dependent Upper-Confidence Bounds for Directed Exploration—0
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation—0
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable—0
Autonomous synthesis of metastable materials—0
Entropy-guided sequence weighting for efficient exploration in RL-based LLM fine-tuning—0
Efficient Policy Space Response Oracles—0
Efficient Pose and Cell Segmentation using Column Generation—0
Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation Using Vision Language Models—0
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