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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 401–425 of 514 papers

TitleStatusHype
Distilling Realizable Students from Unrealizable Teachers—0
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning—0
Distributional Reinforcement Learning for Efficient Exploration—0
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning—0
Divide and Explore: Multi-Agent Separate Exploration with Shared Intrinsic Motivations—0
MAGMA: An Optimization Framework for Mapping Multiple DNNs on Multiple Accelerator Cores—0
DREAM: Decentralized Reinforcement Learning for Exploration and Efficient Energy Management in Multi-Robot Systems—0
DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience—0
Efficient, Decentralized, and Collaborative Multi-Robot Exploration using Optimal Transport Theory—0
EfficientEQA: An Efficient Approach for Open Vocabulary Embodied Question Answering—0
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
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
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