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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 451–475 of 514 papers

TitleStatusHype
NSGA-Net: Neural Architecture Search using Multi-Objective Genetic AlgorithmCode0
On Preemption and Learning in Stochastic SchedulingCode0
ConEx: Efficient Exploration of Big-Data System Configurations for Better PerformanceCode0
Stochastic Gradient Hamiltonian Monte CarloCode0
Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank BanditsCode0
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement LearningCode0
Robust quantum dots charge autotuning using neural network uncertaintyCode0
Strangeness-driven Exploration in Multi-Agent Reinforcement LearningCode0
Online Limited Memory Neural-Linear Bandits with Likelihood MatchingCode0
On Machine Learning-Driven Surrogates for Sound Transmission Loss SimulationsCode0
Lagrangian Manifold Monte Carlo on Monge PatchesCode0
Efficient Gradient-Free Variational Inference using Policy SearchCode0
Efficient Exploration via State Marginal MatchingCode0
Behavior-Guided Actor-Critic: Improving Exploration via Learning Policy Behavior Representation for Deep Reinforcement LearningCode0
Large-Batch, Iteration-Efficient Neural Bayesian Design OptimizationCode0
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement LearningCode0
Concurrent Meta Reinforcement LearningCode0
Efficient Exploration through Bayesian Deep Q-NetworksCode0
Efficient Exploration of the Rashomon Set of Rule Set ModelsCode0
Amortized Variational Deep Q NetworkCode0
A Fast and Scalable Polyatomic Frank-Wolfe Algorithm for the LASSOCode0
Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share?Code0
Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior SamplingCode0
Learning-Driven Exploration for Reinforcement LearningCode0
Learning Dynamic Cognitive Map with Autonomous NavigationCode0
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