SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 1501–1525 of 15113 papers

TitleStatusHype
GAEA: Graph Augmentation for Equitable Access via Reinforcement LearningCode1
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement LearningCode1
Actor Prioritized Experience ReplayCode1
Memory-efficient Reinforcement Learning with Value-based Knowledge ConsolidationCode1
Tactical Optimism and Pessimism for Deep Reinforcement LearningCode1
Deep Reinforcement Learning with Double Q-learningCode1
Generalizable Episodic Memory for Deep Reinforcement LearningCode1
Generalizing Across Multi-Objective Reward Functions in Deep Reinforcement LearningCode1
Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization ProblemsCode1
Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous ControlCode1
Barrier Certified Safety Learning Control: When Sum-of-Square Programming Meets Reinforcement LearningCode1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
Asset Allocation: From Markowitz to Deep Reinforcement LearningCode1
Deep reinforcement learning-designed radiofrequency waveform in MRICode1
Functional Regularization for Reinforcement Learning via Learned Fourier FeaturesCode1
Basis for Intentions: Efficient Inverse Reinforcement Learning using Past ExperienceCode1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
Fully Decentralized Multi-Agent Reinforcement Learning with Networked AgentsCode1
FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement LearningCode1
Mind the Gap: Offline Policy Optimization for Imperfect RewardsCode1
Agent57: Outperforming the Atari Human BenchmarkCode1
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster trainingCode1
Deep Transformer Q-Networks for Partially Observable Reinforcement LearningCode1
Mirror Learning: A Unifying Framework of Policy OptimisationCode1
A Deep Reinforced Model for Abstractive SummarizationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified