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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 176–200 of 514 papers

TitleStatusHype
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning—0
Entropic Risk-Sensitive Reinforcement Learning: A Meta Regret Framework with Function Approximation—0
Entropy-guided sequence weighting for efficient exploration in RL-based LLM fine-tuning—0
Feature Engineering for Predictive Modeling using Reinforcement Learning—0
ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation—0
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization—0
ActiveGAMER: Active GAussian Mapping through Efficient Rendering—0
Evolutionary Reinforcement Learning via Cooperative Coevolution—0
Finding Waldo: Towards Efficient Exploration of NeRF Scene Spaces—0
Explicit Planning for Efficient Exploration in Reinforcement Learning—0
Explicit Recall for Efficient Exploration—0
Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes—0
Exploration by Distributional Reinforcement Learning—0
Exploration by Learning Diverse Skills through Successor State Measures—0
Exploration by Uncertainty in Reward Space—0
Efficient Exploration of Image Classifier Failures with Bayesian Optimization and Text-to-Image Models—0
Exploration in Model-based Reinforcement Learning with Randomized Reward—0
Exploration of the search space of Gaussian graphical models for paired data—0
Exploration via Epistemic Value Estimation—0
Data-Efficient Exploration with Self Play for Atari—0
Exploratory Diffusion Model for Unsupervised Reinforcement Learning—0
Deep Active Ensemble Sampling For Image Classification—0
Explore until Confident: Efficient Exploration for Embodied Question Answering—0
A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints—0
Computing low-thrust transfers in the asteroid belt, a comparison between astrodynamical manipulations and a machine learning approach—0
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