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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 131140 of 514 papers

TitleStatusHype
Hierarchical Spatial Proximity Reasoning for Vision-and-Language NavigationCode0
Diffusion-Reinforcement Learning Hierarchical Motion Planning in Multi-agent Adversarial GamesCode1
MAMBA: an Effective World Model Approach for Meta-Reinforcement LearningCode1
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning0
Scalable Online Exploration via CoverabilityCode0
A Natural Extension To Online Algorithms For Hybrid RL With Limited Coverage0
Noisy Spiking Actor Network for Exploration0
Finding Waldo: Towards Efficient Exploration of NeRF Scene Spaces0
Vlearn: Off-Policy Learning with Efficient State-Value Function Estimation0
Cradle: Empowering Foundation Agents Towards General Computer ControlCode7
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