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 1801–1825 of 15113 papers

TitleStatusHype
Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven OptimizationCode1
Quantifying Generalization in Reinforcement LearningCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit DesignCode1
Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code SynthesisCode1
Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulationsCode1
Conditional Mutual Information for Disentangled Representations in Reinforcement LearningCode1
Consistency Models as a Rich and Efficient Policy Class for Reinforcement LearningCode1
Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation ErrorsCode1
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy SearchCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Rainier: Reinforced Knowledge Introspector for Commonsense Question AnsweringCode1
Randomized Exploration for Reinforcement Learning with General Value Function ApproximationCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
RealAnt: An Open-Source Low-Cost Quadruped for Education and Research in Real-World Reinforcement LearningCode1
Combining Modular Skills in Multitask LearningCode1
ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented GenerationCode1
Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data ContaminationCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
Recomposing the Reinforcement Learning Building Blocks with HypernetworksCode1
A coevolutionary approach to deep multi-agent reinforcement learningCode1
Redeeming Intrinsic Rewards via Constrained OptimizationCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
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Benchmark Results

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