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Found 8 papers, 0 papers with code
Dynamic Memory-based Curiosity: A Bootstrap Approach for Exploration
The curiosity arouses if memorized information can not deal with the current state, and the information gap between dual learners can be formulated as the intrinsic reward for agents, and then such state information can be consolidated into the dynamic memory.
Self-Supervised Exploration via Temporal Inconsistency in Reinforcement Learning
Previous attempts suggest that intrinsic reward can alleviate the issue caused by sparsity.
Nuclear Norm Maximization Based Curiosity-Driven Learning
To handle the sparsity of the extrinsic rewards in reinforcement learning, researchers have proposed intrinsic reward which enables the agent to learn the skills that might come in handy for pursuing the rewards in the future, such as encouraging the agent to visit novel states.
CLIP2TV: Align, Match and Distill for Video-Text Retrieval
Modern video-text retrieval frameworks basically consist of three parts: video encoder, text encoder and the similarity head.
Ranked #9 on
Video Retrieval
on MSR-VTT
(using extra training data)
Coarse to Fine: Video Retrieval before Moment Localization
The current state-of-the-art methods for video corpus moment retrieval (VCMR) often use similarity-based feature alignment approach for the sake of convenience and speed.
KnowSR: Knowledge Sharing among Homogeneous Agents in Multi-agent Reinforcement Learning
In this paper, we present an adaptation method of the majority of multi-agent reinforcement learning (MARL) algorithms called KnowSR which takes advantage of the differences in learning between agents.
Knowledge Distillation
Multi-agent Reinforcement Learning
+2
KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learning
In this paper, we propose a method, named "KnowRU" for knowledge reusing which can be easily deployed in the majority of the multi-agent reinforcement learning algorithms without complicated hand-coded design.
Knowledge Distillation
Multi-agent Reinforcement Learning
+2
Random forest model identifies serve strength as a key predictor of tennis match outcome
We compiled, cleaned, and used the largest database of tennis match information to date to predict match outcome using fairly simple machine learning methods.
