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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

Showing 451–500 of 514 papers

TitleStatusHype
Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II—0
Learning to Interrupt: A Hierarchical Deep Reinforcement Learning Framework for Efficient Exploration—0
New/s/leak 2.0 - Multilingual Information Extraction and Visualization for Investigative Journalism—0
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision ProcessesCode0
Goal-oriented Trajectories for Efficient Exploration—0
Curiosity Driven Exploration of Learned Disentangled Goal SpacesCode0
Efficient Gradient-Free Variational Inference using Policy SearchCode0
Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse RewardsCode0
Scheduled Policy Optimization for Natural Language Communication with Intelligent AgentsCode0
Meta-Learning for Stochastic Gradient MCMCCode0
Randomized Value Functions via Multiplicative Normalizing FlowsCode0
A Web-scale system for scientific knowledge exploration—0
When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms—0
Efficient Exploration of Gradient Space for Online Learning to Rank—0
Exploration by Distributional Reinforcement Learning—0
A Human Mixed Strategy Approach to Deep Reinforcement Learning—0
Variance Networks: When Expectation Does Not Meet Your ExpectationsCode0
Dimension-Robust MCMC in Bayesian Inverse Problems—0
Efficient Exploration through Bayesian Deep Q-NetworksCode0
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning—0
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement LearningCode0
Federated Control with Hierarchical Multi-Agent Deep Reinforcement LearningCode0
The Eigenoption-Critic Framework—0
Reinforced dynamics for enhanced sampling in large atomic and molecular systems—0
Noisy Natural Gradient as Variational InferenceCode0
Uncertainty Estimates for Efficient Neural Network-based Dialogue Policy Optimisation—0
Variational Deep Q NetworkCode0
Efficient exploration with Double Uncertain Value Networks—0
A Compression-Inspired Framework for Macro Discovery—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Deep density networks and uncertainty in recommender systems—0
Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression—0
Feature Engineering for Predictive Modeling using Reinforcement Learning—0
Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for MCMC—0
Hashing over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning—0
Protein design by multiobjective optimization: evolutionary and non-evolutionary approaches—0
Life-iNet: A Structured Network-Based Knowledge Exploration and Analytics System for Life Sciences—0
Noisy Networks for ExplorationCode0
Count-Based Exploration in Feature Space for Reinforcement LearningCode0
Fractional Langevin Monte Carlo: Exploring Lévy Driven Stochastic Differential Equations for Markov Chain Monte Carlo—0
K-Means Clustering using Tabu Search with Quantized Means—0
Deep Exploration via Randomized Value Functions—0
Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted IlluminationCode0
Efficient Pose and Cell Segmentation using Column Generation—0
Contextual Decision Processes with Low Bellman Rank are PAC-Learnable—0
Hands-Free Segmentation of Medical Volumes via Binary Inputs—0
Processing Document Collections to Automatically Extract Linked Data: Semantic Storytelling Technologies for Smart Curation Workflows—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Deep Exploration via Bootstrapped DQNCode0
Angrier Birds: Bayesian reinforcement learningCode0
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