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 851–875 of 15113 papers

TitleStatusHype
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
Extreme Q-Learning: MaxEnt RL without EntropyCode1
Emergent collective intelligence from massive-agent cooperation and competitionCode1
Learning to Maximize Mutual Information for Dynamic Feature SelectionCode1
Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous NavigationCode1
Environment Agnostic Representation for Visual Reinforcement LearningCode1
Self-Activating Neural Ensembles for Continual Reinforcement LearningCode1
Transformer in Transformer as Backbone for Deep Reinforcement LearningCode1
Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression SearchCode1
Risk-Sensitive Policy with Distributional Reinforcement LearningCode1
Lexicographic Multi-Objective Reinforcement LearningCode1
On Pathologies in KL-Regularized Reinforcement Learning from Expert DemonstrationsCode1
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
Critic-Guided Decoding for Controlled Text GenerationCode1
Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence SummarizationCode1
On Reinforcement Learning for the Game of 2048Code1
Offline Reinforcement Learning for Visual NavigationCode1
Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand SystemsCode1
Reinforcement Learning and Tree Search Methods for the Unit Commitment ProblemCode1
MoDem: Accelerating Visual Model-Based Reinforcement Learning with DemonstrationsCode1
Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model CheckingCode1
Compiler Optimization for Quantum Computing Using Reinforcement LearningCode1
PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User EngagementCode1
Physics-Informed Model-Based Reinforcement LearningCode1
RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement LearningCode1
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Benchmark Results

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