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 10851–10875 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
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Benchmark Results

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