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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 401–450 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
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
Efficient gPC-based quantification of probabilistic robustness for systems in neuroscience—0
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation—0
Efficient Policy Space Response Oracles—0
Efficient Pose and Cell Segmentation using Column Generation—0
Reinforcement Learning for Causal Discovery without Acyclicity Constraints—0
Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization—0
Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling—0
Embodied Agents for Efficient Exploration and Smart Scene Description—0
Emotion-Agent: Unsupervised Deep Reinforcement Learning with Distribution-Prototype Reward for Continuous Emotional EEG Analysis—0
End-Effect Exploration Drive for Effective Motor Learning—0
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning—0
Entropic Risk-Sensitive Reinforcement Learning: A Meta Regret Framework with Function Approximation—0
Entropy-guided sequence weighting for efficient exploration in RL-based LLM fine-tuning—0
ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation—0
Evolutionary Reinforcement Learning via Cooperative Coevolution—0
Explicit Planning for Efficient Exploration in Reinforcement Learning—0
Explicit Recall for Efficient Exploration—0
Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes—0
Exploration by Distributional Reinforcement Learning—0
Exploration by Learning Diverse Skills through Successor State Measures—0
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