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 2601–2625 of 15113 papers

TitleStatusHype
Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning—0
Adaptive Diffusion Policy Optimization for Robotic ManipulationCode0
DSADF: Thinking Fast and Slow for Decision Making—0
Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles—0
Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning—0
Reinforcement Learning-based Fault-Tolerant Control for Quadrotor with Online Transformer Adaptation—0
The Exploratory Multi-Asset Mean-Variance Portfolio Selection using Reinforcement Learning—0
Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning—0
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review—0
INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning—0
DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic RewardCode0
Cache-Efficient Posterior Sampling for Reinforcement Learning with LLM-Derived Priors Across Discrete and Continuous Domains—0
Design and Experimental Test of Datatic Approximate Optimal Filter in Nonlinear Dynamic Systems—0
Learning Value of Information towards Joint Communication and Control in 6G V2X—0
FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots—0
X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real—0
Reinforcement Learning (RL) Meets Urban Climate Modeling: Investigating the Efficacy and Impacts of RL-Based HVAC Control—0
Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving—0
LineFlow: A Framework to Learn Active Control of Production LinesCode0
REFINE-AF: A Task-Agnostic Framework to Align Language Models via Self-Generated Instructions using Reinforcement Learning from Automated Feedback—0
Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach—0
Interaction-Aware Parameter Privacy-Preserving Data Sharing in Coupled Systems via Particle Filter Reinforcement Learning—0
Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients—0
Active Perception for Tactile Sensing: A Task-Agnostic Attention-Based Approach—0
Pretraining a Shared Q-Network for Data-Efficient Offline Reinforcement Learning—0
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Benchmark Results

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