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 4301–4325 of 15113 papers

TitleStatusHype
Attention Graph for Multi-Robot Social Navigation with Deep Reinforcement Learning—0
A Reinforcement Learning Based Controller to Minimize Forces on the Crutches of a Lower-Limb Exoskeleton—0
A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC GuaranteesCode0
Safe Reinforcement Learning-Based Eco-Driving Control for Mixed Traffic Flows With Disturbances—0
Zero-Shot Reinforcement Learning via Function EncodersCode0
Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control—0
Augmenting Replay in World Models for Continual Reinforcement LearningCode0
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
On the Limitations of Markovian Rewards to Express Multi-Objective, Risk-Sensitive, and Modal Tasks—0
Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement LearningCode0
Hierarchical Continual Reinforcement Learning via Large Language Model—0
Learning fast changing slow in spiking neural networks—0
Learning-based sensing and computing decision for data freshness in edge computing-enabled networks—0
Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation—0
Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research—0
Sample Efficient Reinforcement Learning by Automatically Learning to Compose Subtasks—0
HMM for Discovering Decision-Making Dynamics Using Reinforcement Learning ExperimentsCode0
On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization—0
Towards Socially and Morally Aware RL agent: Reward Design With LLM—0
Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning—0
Learning safety critics via a non-contractive binary bellman operator—0
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Benchmark Results

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