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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 51–75 of 514 papers

TitleStatusHype
Mapping Galaxy Images Across Ultraviolet, Visible and Infrared Bands Using Generative Deep LearningCode0
Multi-Objective Hyperparameter Selection via Hypothesis Testing on Reliability GraphsCode0
Bridging Text and Crystal Structures: Literature-driven Contrastive Learning for Materials Science—0
ActiveGAMER: Active GAussian Mapping through Efficient Rendering—0
β-DQN: Improving Deep Q-Learning By Evolving the Behavior—0
Provably Efficient Exploration in Reward Machines with Low Regret—0
A diversity-enhanced genetic algorithm for efficient exploration of parameter spacesCode0
GraphEQA: Using 3D Semantic Scene Graphs for Real-time Embodied Question Answering—0
GenPlan: Generative Sequence Models as Adaptive PlannersCode0
A Temporally Correlated Latent Exploration for Reinforcement Learning—0
Hyper: Hyperparameter Robust Efficient Exploration in Reinforcement Learning—0
Sample Efficient Robot Learning in Supervised Effect Prediction Tasks—0
CBOL-Tuner: Classifier-pruned Bayesian optimization to explore temporally structured latent spaces for particle accelerator tuning—0
Adaptformer: Sequence models as adaptive iterative planners—0
Randomized-Grid Search for Hyperparameter Tuning in Decision Tree Model to Improve Performance of Cardiovascular Disease Classification—0
BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem SolvingCode0
Umbrella Reinforcement Learning -- computationally efficient tool for hard non-linear problemsCode0
Learning Dynamic Cognitive Map with Autonomous NavigationCode0
Scalable Sampling for High Utility PatternsCode0
Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL—0
EfficientEQA: An Efficient Approach for Open Vocabulary Embodied Question Answering—0
Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration—0
Leveraging Skills from Unlabeled Prior Data for Efficient Online ExplorationCode1
Scattered Forest Search: Smarter Code Space Exploration with LLMs—0
TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement LearningCode0
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