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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6626–6650 of 15113 papers

TitleStatusHype
To Switch or Not to Switch? Balanced Policy Switching in Offline Reinforcement Learning—0
Total stochastic gradient algorithms and applications in reinforcement learning—0
To the Noise and Back: Diffusion for Shared Autonomy—0
ToTRL: Unlock LLM Tree-of-Thoughts Reasoning Potential through Puzzles Solving—0
Tournament selection in zeroth-level classifier systems based on average reward reinforcement learning—0
Toward a Reinforcement-Learning-Based System for Adjusting Medication to Minimize Speech Disfluency—0
Toward Compositional Generalization in Object-Oriented World Modeling—0
Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping—0
Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control—0
Toward Effective Reinforcement Learning Fine-Tuning for Medical VQA in Vision-Language Models—0
Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challenges—0
Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control—0
Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees—0
Toward negotiable reinforcement learning: shifting priorities in Pareto optimal sequential decision-making—0
Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning—0
Toward Real-Time Decentralized Reinforcement Learning using Finite Support Basis Functions—0
Toward Reliable Designs of Data-Driven Reinforcement Learning Tracking Control for Euler-Lagrange Systems—0
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning—0
Towards a Better Understanding of Representation Dynamics under TD-learning—0
Towards a Deep Reinforcement Learning Approach for Tower Line Wars—0
Towards a Formal Theory of the Need for Competence via Computational Intrinsic Motivation—0
Towards a Fully Autonomous UAV Controller for Moving Platform Detection and Landing—0
Towards a General Framework for ML-based Self-tuning Databases—0
Towards AI-controlled FES-restoration of arm movements: Controlling for progressive muscular fatigue with Gaussian state-space models—0
Towards AI-controlled FES-restoration of arm movements: neuromechanics-based reinforcement learning for 3-D reaching—0
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Benchmark Results

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