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 2501–2550 of 15113 papers

TitleStatusHype
M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic ManipulationCode1
Zero-Shot Reinforcement Learning via Function EncodersCode0
Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control—0
Context-Former: Stitching via Latent Conditioned Sequence Modeling—0
The Indoor-Training Effect: unexpected gains from distribution shifts in the transition function—0
SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning—0
LEACH-RLC: Enhancing IoT Data Transmission with Optimized Clustering and Reinforcement LearningCode0
Social Interpretable Reinforcement Learning—0
Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement LearningCode0
On the Limitations of Markovian Rewards to Express Multi-Objective, Risk-Sensitive, and Modal Tasks—0
Learning fast changing slow in spiking neural networks—0
Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research—0
Hierarchical Continual Reinforcement Learning via Large Language Model—0
Sample Efficient Reinforcement Learning by Automatically Learning to Compose Subtasks—0
Learning-based sensing and computing decision for data freshness in edge computing-enabled networks—0
True Knowledge Comes from Practice: Aligning LLMs with Embodied Environments via Reinforcement LearningCode2
HMM for Discovering Decision-Making Dynamics Using Reinforcement Learning ExperimentsCode0
Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation—0
SEER: Facilitating Structured Reasoning and Explanation via Reinforcement LearningCode1
DittoGym: Learning to Control Soft Shape-Shifting RobotsCode1
A Safe Reinforcement Learning Algorithm for Supervisory Control of Power Plants—0
Stable and Safe Human-aligned Reinforcement Learning through Neural Ordinary Differential EquationsCode1
HAZARD Challenge: Embodied Decision Making in Dynamically Changing EnvironmentsCode1
On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization—0
Learning safety critics via a non-contractive binary bellman operator—0
Towards Socially and Morally Aware RL agent: Reward Design With LLM—0
Active Inference as a Model of Agency—0
Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning—0
Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid AlgorithmsCode3
Constrained Reinforcement Learning for Adaptive Controller Synchronization in Distributed SDN—0
Emergent Dominance Hierarchies in Reinforcement Learning AgentsCode0
Solving Offline Reinforcement Learning with Decision Tree RegressionCode0
Back-stepping Experience Replay with Application to Model-free Reinforcement Learning for a Soft Snake Robot—0
Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement LearningCode1
Information-Theoretic State Variable Selection for Reinforcement LearningCode0
VQC-Based Reinforcement Learning with Data Re-uploading: Performance and TrainabilityCode0
Large-scale Reinforcement Learning for Diffusion Models—0
Closing the Gap between TD Learning and Supervised Learning -- A Generalisation Point of ViewCode1
FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough ReproductionCode0
Crowd-PrefRL: Preference-Based Reward Learning from Crowds—0
Blackout Mitigation via Physics-guided RLCode0
Deployable Reinforcement Learning with Variable Control RateCode0
DeLF: Designing Learning Environments with Foundation ModelsCode0
Learning from Imperfect Demonstrations with Self-Supervision for Robotic Manipulation—0
UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender SystemsCode1
Bridging State and History Representations: Understanding Self-Predictive RLCode1
Efficient Training of Generalizable Visuomotor Policies via Control-Aware Augmentation—0
Learning from Sparse Offline Datasets via Conservative Density EstimationCode0
PRewrite: Prompt Rewriting with Reinforcement Learning—0
CycLight: learning traffic signal cooperation with a cycle-level strategy—0
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Benchmark Results

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