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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 421–430 of 514 papers

TitleStatusHype
A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints—0
Efficient Exploration of Image Classifier Failures with Bayesian Optimization and Text-to-Image Models—0
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization—0
Efficient exploration of zero-sum stochastic games—0
Efficient Exploration through Intrinsic Motivation Learning for Unsupervised Subgoal Discovery in Model-Free Hierarchical Reinforcement Learning—0
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization—0
Efficient Exploration using Model-Based Quality-Diversity with Gradients—0
Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization—0
Efficient exploration with Double Uncertain Value Networks—0
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards—0
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