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 10851–10900 of 15113 papers

TitleStatusHype
To bootstrap or to rollout? An optimal and adaptive interpolation—0
To Combine or Not To Combine? A Rainbow Deep Reinforcement Learning Agent for Dialog Policies—0
Toddler-Guidance Learning: Impacts of Critical Period on Multimodal AI Agents—0
Together We Rise: Optimizing Real-Time Multi-Robot Task Allocation using Coordinated Heterogeneous Plays—0
Toggling a Genetic Switch Using Reinforcement Learning—0
Token-Efficient RL for LLM Reasoning—0
Token-Mol 1.0: Tokenized drug design with large language model—0
Tolerance of Reinforcement Learning Controllers against Deviations in Cyber Physical Systems—0
TOMA: Topological Map Abstraction for Reinforcement Learning—0
Toolpath design for additive manufacturing using deep reinforcement learning—0
Topic-Preserving Synthetic News Generation: An Adversarial Deep Reinforcement Learning Approach—0
To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs—0
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
Towards a Metric for Automated Conversational Dialogue System Evaluation and Improvement—0
Towards an Adaptable and Generalizable Optimization Engine in Decision and Control: A Meta Reinforcement Learning Approach—0
Towards an Adaptive Robot for Sports and Rehabilitation Coaching—0
Towards an Interpretable Hierarchical Agent Framework using Semantic Goals—0
Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble—0
Towards a practical measure of interference for reinforcement learning—0
A step toward a reinforcement learning de novo genome assembler—0
Towards a Sample Efficient Reinforcement Learning Pipeline for Vision Based Robotics—0
Towards a Simple Approach to Multi-step Model-based Reinforcement Learning—0
Towards a Solution to Bongard Problems: A Causal Approach—0
Towards a Sustainable Internet-of-Underwater-Things based on AUVs, SWIPT, and Reinforcement Learning—0
Towards a Theoretical Foundation of Policy Optimization for Learning Control Policies—0
Towards A Unified Agent with Foundation Models—0
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Benchmark Results

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