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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 351–400 of 514 papers

TitleStatusHype
Bandit Algorithms for Tree Search—0
BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale—0
Bayesian optimisation of large-scale photonic reservoir computers—0
Bayesian optimization of distributed neurodynamical controller models for spatial navigation—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
β-DQN: Improving Deep Q-Learning By Evolving the Behavior—0
Better Exploration with Optimistic Actor-Critic—0
Beyond Games: Bringing Exploration to Robots in Real-world—0
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning—0
Biased Estimates of Advantages over Path Ensembles—0
BooVI: Provably Efficient Bootstrapped Value Iteration—0
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization—0
CAE: Repurposing the Critic as an Explorer in Deep Reinforcement Learning—0
Causal Information Prioritization for Efficient Reinforcement Learning—0
CBOL-Tuner: Classifier-pruned Bayesian optimization to explore temporally structured latent spaces for particle accelerator tuning—0
HelixMO: Sample-Efficient Molecular Optimization in Scene-Sensitive Latent Space—0
CIM: Constrained Intrinsic Motivation for Sparse-Reward Continuous Control—0
Clustered Reinforcement Learning—0
Comprehensive decision-strategy space exploration for efficient territorial planning strategies—0
Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs—0
Computing low-thrust transfers in the asteroid belt, a comparison between astrodynamical manipulations and a machine learning approach—0
Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation Using Vision Language Models—0
Constrained Hybrid Metaheuristic Algorithm for Probabilistic Neural Networks Learning—0
Context-Dependent Upper-Confidence Bounds for Directed Exploration—0
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable—0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
Bridging Text and Crystal Structures: Literature-driven Contrastive Learning for Materials Science—0
Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning—0
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning—0
Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation—0
CURO: Curriculum Learning for Relative Overgeneralization—0
Data-Efficient Exploration with Self Play for Atari—0
Deep Active Ensemble Sampling For Image Classification—0
Deep density networks and uncertainty in recommender systems—0
Deep exploration by novelty-pursuit with maximum state entropy—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
Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching—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
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous and Instruction-guided Driving—0
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
Directed Exploration for Reinforcement Learning—0
Directed Exploration in PAC Model-Free Reinforcement Learning—0
DISCO-10M: A Large-Scale Music Dataset—0
Discovering Context Specific Causal Relationships—0
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