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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 451–500 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
Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement LearningCode0
OTO Planner: An Efficient Only Travelling Once Exploration Planner for Complex and Unknown EnvironmentsCode0
Uncertainty-Guided Optimization on Large Language Model Search TreesCode0
Model-based Reinforcement Learning for Continuous Control with Posterior SamplingCode0
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement LearningCode0
A Gradient Sampling Algorithm for Stratified Maps with Applications to Topological Data AnalysisCode0
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement LearningCode0
Parameterized Indexed Value Function for Efficient Exploration in Reinforcement LearningCode0
Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial OptimizationCode0
Scalable Online Exploration via CoverabilityCode0
Learning to Act with Affordance-Aware Multimodal Neural SLAMCode0
Scalable Sampling for High Utility PatternsCode0
BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem SolvingCode0
Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone MicrocontrollerCode0
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient ExplorationCode0
Bootstrapped Meta-LearningCode0
TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement LearningCode0
Disentangling Uncertainties by Learning Compressed Data RepresentationCode0
Playing Text-Adventure Games with Graph-Based Deep Reinforcement LearningCode0
LECO: Learnable Episodic Count for Task-Specific Intrinsic RewardCode0
Discovering and Exploiting Sparse Rewards in a Learned Behavior SpaceCode0
Bayesian Curiosity for Efficient Exploration in Reinforcement LearningCode0
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesisCode0
DISCOVER: Automated Curricula for Sparse-Reward Reinforcement LearningCode0
Deep Exploration via Bootstrapped DQNCode0
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