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 1001–1025 of 15113 papers

TitleStatusHype
Interactive Machine Learning of Musical GestureCode1
Consistency Models as a Rich and Efficient Policy Class for Reinforcement LearningCode1
Game-Theoretic Multiagent Reinforcement LearningCode1
Interpretable Concept Bottlenecks to Align Reinforcement Learning AgentsCode1
Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement LearningCode1
Conservative Q-Learning for Offline Reinforcement LearningCode1
Conservative Offline Distributional Reinforcement LearningCode1
Zero-Shot Reinforcement Learning from Low Quality DataCode1
Investigating practical linear temporal difference learningCode1
Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement LearningCode1
Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM ReasoningCode1
Is Q-learning Provably Efficient?Code1
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint GeneratorsCode1
Iterative Amortized Policy OptimizationCode1
ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement LearningCode1
Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary StrategiesCode1
Adaptive Transformers in RLCode1
Analytical Lyapunov Function Discovery: An RL-based Generative ApproachCode1
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
Jump-Start Reinforcement LearningCode1
Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous ControlCode1
Kalman meets Bellman: Improving Policy Evaluation through Value TrackingCode1
Computational Performance of Deep Reinforcement Learning to find Nash EquilibriaCode1
Advancing Multimodal Reasoning via Reinforcement Learning with Cold StartCode1
Concise Reasoning via Reinforcement LearningCode1
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Benchmark Results

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