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 1276–1300 of 15113 papers

TitleStatusHype
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
Combining Modular Skills in Multitask LearningCode1
A Practical Two-Stage Recipe for Mathematical LLMs: Maximizing Accuracy with SFT and Efficiency with Reinforcement LearningCode1
Avalanche RL: a Continual Reinforcement Learning LibraryCode1
Entropy-Regularized Process Reward ModelCode1
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
Compiler Optimization for Quantum Computing Using Reinforcement LearningCode1
Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learningCode1
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningCode1
GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI AgentsCode1
Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution SystemsCode1
Accelerating Exploration with Unlabeled Prior DataCode1
Compile Scene Graphs with Reinforcement LearningCode1
Evolutionary Planning in Latent SpaceCode1
A Production Scheduling Framework for Reinforcement Learning Under Real-World ConstraintsCode1
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement LearningCode1
CompoSuite: A Compositional Reinforcement Learning BenchmarkCode1
Adversarial Deep Reinforcement Learning in Portfolio ManagementCode1
Compositional Reinforcement Learning from Logical SpecificationsCode1
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement LearningCode1
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement LearningCode1
Enhancing RL Safety with Counterfactual LLM ReasoningCode1
Hearts Gym: Learning Reinforcement Learning as a Team EventCode1
Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational GraphCode1
An Experimental Design Perspective on Model-Based Reinforcement LearningCode1
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Benchmark Results

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