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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 101–150 of 514 papers

TitleStatusHype
Evolutionary Reinforcement Learning via Cooperative Coevolution—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Deep Exploration via Randomized Value Functions—0
DEEPGONET: Multi-label Prediction of GO Annotation for Protein from Sequence Using Cascaded Convolutional and Recurrent Network—0
Exploratory Diffusion Model for Unsupervised Reinforcement Learning—0
BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale—0
Design of Convolutional Extreme Learning Machines for Vision-Based Navigation Around Small Bodies—0
Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning—0
Differentially Evolving Memory Ensembles: Pareto Optimization based on Computational Intelligence for Embedded Memories on a System Level—0
DiffExp: Efficient Exploration in Reward Fine-tuning for Text-to-Image Diffusion Models—0
Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning—0
Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation—0
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous and Instruction-guided Driving—0
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
End-Effect Exploration Drive for Effective Motor Learning—0
Directed Exploration for Reinforcement Learning—0
Directed Exploration in PAC Model-Free Reinforcement Learning—0
DISCO-10M: A Large-Scale Music Dataset—0
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration—0
KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance—0
Discovering Context Specific Causal Relationships—0
BooVI: Provably Efficient Bootstrapped Value Iteration—0
Distilling Realizable Students from Unrealizable Teachers—0
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning—0
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning—0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
Emotion-Agent: Unsupervised Deep Reinforcement Learning with Distribution-Prototype Reward for Continuous Emotional EEG Analysis—0
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning—0
Bandit Algorithms for Tree Search—0
Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning—0
Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling—0
Reinforcement Learning for Causal Discovery without Acyclicity Constraints—0
Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization—0
Embodied Agents for Efficient Exploration and Smart Scene Description—0
Entropic Risk-Sensitive Reinforcement Learning: A Meta Regret Framework with Function Approximation—0
Bag of Policies for Distributional Deep Exploration—0
Bridging Text and Crystal Structures: Literature-driven Contrastive Learning for Materials Science—0
A Human Mixed Strategy Approach to Deep Reinforcement Learning—0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
A Web-scale system for scientific knowledge exploration—0
Efficient Policy Space Response Oracles—0
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable—0
Context-Dependent Upper-Confidence Bounds for Directed Exploration—0
Active Model Estimation in Markov Decision Processes—0
Constrained Hybrid Metaheuristic Algorithm for Probabilistic Neural Networks Learning—0
AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation—0
ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization—0
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation—0
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