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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 251–300 of 514 papers

TitleStatusHype
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization—0
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation—0
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret—0
Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization—0
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning—0
Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs—0
GFlowNets for AI-Driven Scientific Discovery—0
ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation—0
Embodied Agents for Efficient Exploration and Smart Scene Description—0
Exploration in Model-based Reinforcement Learning with Randomized Reward—0
Strangeness-driven Exploration in Multi-Agent Reinforcement LearningCode0
SHIRO: Soft Hierarchical Reinforcement Learning—0
Reinforcement Learning in Credit Scoring and Underwriting—0
Efficient Exploration in Resource-Restricted Reinforcement Learning—0
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning—0
CURO: Curriculum Learning for Relative Overgeneralization—0
HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression—0
Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions—0
CIM: Constrained Intrinsic Motivation for Sparse-Reward Continuous Control—0
Efficient Exploration using Model-Based Quality-Diversity with Gradients—0
Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation—0
Exploring through Random Curiosity with General Value FunctionsCode0
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction—0
Design of Convolutional Extreme Learning Machines for Vision-Based Navigation Around Small Bodies—0
Deep Active Ensemble Sampling For Image Classification—0
LECO: Learnable Episodic Count for Task-Specific Intrinsic RewardCode0
The Role of Coverage in Online Reinforcement Learning—0
Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning—0
Self-supervised Sequential Information Bottleneck for Robust Exploration in Deep Reinforcement Learning—0
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement LearningCode0
Impact Makes a Sound and Sound Makes an Impact: Sound Guides Representations and ExplorationsCode0
Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks—0
SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks—0
The split Gibbs sampler revisited: improvements to its algorithmic structure and augmented target distributionCode0
GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning—0
Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation—0
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback—0
Sample-Efficient, Exploration-Based Policy Optimisation for Routing Problems—0
On Preemption and Learning in Stochastic SchedulingCode0
Personalized Algorithmic Recourse with Preference ElicitationCode0
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning—0
Feature and Instance Joint Selection: A Reinforcement Learning Perspective—0
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based GamesCode0
On Machine Learning-Driven Surrogates for Sound Transmission Loss SimulationsCode0
A Variational Approach to Bayesian Phylogenetic InferenceCode0
Efficient Exploration via First-Person Behavior Cloning Assisted Rapidly-Exploring Random Trees—0
TANDEM: Learning Joint Exploration and Decision Making with Tactile Sensors—0
Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share?Code0
Learning Causal Overhypotheses through Exploration in Children and Computational Models—0
A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search—0
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