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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 301–350 of 514 papers

TitleStatusHype
Sparse graphs using exchangeable random measures—0
Misspecification-robust likelihood-free inference in high dimensions—0
n-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank—0
Structured exploration in the finite horizon linear quadratic dual control problem—0
Successor-Predecessor Intrinsic Exploration—0
Synergistic Fusion of Multi-Source Knowledge via Evidence Theory for High-Entropy Alloy Discovery—0
TANDEM: Learning Joint Exploration and Decision Making with Tactile Sensors—0
Targeting the partition function of chemically disordered materials with a generative approach based on inverse variational autoencoders—0
Task-agnostic Exploration in Reinforcement Learning—0
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning—0
The Eigenoption-Critic Framework—0
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors—0
The Role of Coverage in Online Reinforcement Learning—0
The University of Cambridge Russian-English System at WMT13—0
Thompson Sampling Algorithms for Cascading Bandits—0
TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments—0
Towards A Unified Agent with Foundation Models—0
Uncertainty Estimates for Efficient Neural Network-based Dialogue Policy Optimisation—0
Reinforcement Learning in Credit Scoring and Underwriting—0
Using Non-Stationary Bandits for Learning in Repeated Cournot Games with Non-Stationary Demand—0
Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning—0
VASE: Variational Assorted Surprise Exploration for Reinforcement Learning—0
VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts—0
Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression—0
Virtual Action Actor-Critic Framework for Exploration (Student Abstract)—0
Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning—0
Vlearn: Off-Policy Learning with Efficient State-Value Function Estimation—0
Volumetric Spanners: an Efficient Exploration Basis for Learning—0
Weakly-Supervised Reinforcement Learning for Controllable Behavior—0
When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms—0
Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects—0
WoMAP: World Models For Embodied Open-Vocabulary Object Localization—0
World Models with Hints of Large Language Models for Goal Achieving—0
KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance—0
Finding Waldo: Towards Efficient Exploration of NeRF Scene Spaces—0
Finedeep: Mitigating Sparse Activation in Dense LLMs via Multi-Layer Fine-Grained Experts—0
FIT-SLAM -- Fisher Information and Traversability estimation-based Active SLAM for exploration in 3D environments—0
f-Policy Gradients: A General Framework for Goal Conditioned RL using f-Divergences—0
Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for MCMC—0
Fractional Langevin Monte Carlo: Exploring Lévy Driven Stochastic Differential Equations for Markov Chain Monte Carlo—0
FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching—0
From Automation to Autonomy in Smart Manufacturing: A Bayesian Optimization Framework for Modeling Multi-Objective Experimentation and Sequential Decision Making—0
From proprioception to long-horizon planning in novel environments: A hierarchical RL model—0
GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning—0
GFlowNets for AI-Driven Scientific Discovery—0
GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices—0
Goal-oriented Trajectories for Efficient Exploration—0
Go-Browse: Training Web Agents with Structured Exploration—0
Go-Explore for Residential Energy Management—0
GraphEQA: Using 3D Semantic Scene Graphs for Real-time Embodied Question Answering—0
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