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 1251–1275 of 15113 papers

TitleStatusHype
Faster Deep Reinforcement Learning with Slower Online NetworkCode1
Learning multiple gaits of quadruped robot using hierarchical reinforcement learningCode1
VMAgent: Scheduling Simulator for Reinforcement LearningCode1
An Experimental Design Perspective on Model-Based Reinforcement LearningCode1
ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical PerspectivesCode1
Tell me why! Explanations support learning relational and causal structureCode1
Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless NetworksCode1
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC TasksCode1
Functional Regularization for Reinforcement Learning via Learned Fourier FeaturesCode1
Hierarchical Reinforcement Learning with Timed SubgoalsCode1
Enhancement of a state-of-the-art RL-based detection algorithm for Massive MIMO radarsCode1
Efficient Pressure: Improving efficiency for signalized intersectionsCode1
Reinforcement Learning-Based Automatic Berthing SystemCode1
Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and ClassificationCode1
Automatic Data Augmentation for Generalization in Reinforcement LearningCode1
BCORLE(): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce MarketCode1
NEORL: NeuroEvolution Optimization with Reinforcement LearningCode1
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming SeedingCode1
EDGE: Explaining Deep Reinforcement Learning PoliciesCode1
Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement LearningCode1
Offline Model-based Adaptable Policy LearningCode1
Regularized Softmax Deep Multi-Agent Q-LearningCode1
Learning Long-Term Reward Redistribution via Randomized Return DecompositionCode1
Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor RectificationCode1
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
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Benchmark Results

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