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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 151–160 of 514 papers

TitleStatusHype
Entropic Risk-Sensitive Reinforcement Learning: A Meta Regret Framework with Function Approximation—0
Exploration by Learning Diverse Skills through Successor State Measures—0
Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization—0
Efficient Exploration using Model-Based Quality-Diversity with Gradients—0
Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization—0
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization—0
Efficient Exploration through Intrinsic Motivation Learning for Unsupervised Subgoal Discovery in Model-Free Hierarchical Reinforcement Learning—0
Autonomous synthesis of metastable materials—0
Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling—0
Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation Using Vision Language Models—0
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