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 526–550 of 15113 papers

TitleStatusHype
AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning—0
Rulebook: bringing co-routines to reinforcement learning environmentsCode2
Interactive Double Deep Q-network: Integrating Human Interventions and Evaluative Predictions in Reinforcement Learning of Autonomous Driving—0
An Automated Reinforcement Learning Reward Design Framework with Large Language Model for Cooperative Platoon Coordination—0
LLMs for Engineering: Teaching Models to Design High Powered Rockets—0
BQSched: A Non-intrusive Scheduler for Batch Concurrent Queries via Reinforcement LearningCode0
Neurophysiologically Realistic Environment for Comparing Adaptive Deep Brain Stimulation Algorithms in Parkinson DiseaseCode1
Explainable AI for UAV Mobility Management: A Deep Q-Network Approach for Handover Minimization—0
Depth-Constrained ASV Navigation with Deep RL and Limited Sensing—0
LLM-hRIC: LLM-empowered Hierarchical RAN Intelligent Control for O-RAN—0
CaRL: Learning Scalable Planning Policies with Simple RewardsCode2
RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement LearningCode7
Training Large Language Models to Reason via EM Policy Gradient—0
SAPO-RL: Sequential Actuator Placement Optimization for Fuselage Assembly via Reinforcement Learning—0
Integrating Learning-Based Manipulation and Physics-Based Locomotion for Whole-Body Badminton Robot Control—0
Monte Carlo Planning with Large Language Model for Text-Based Game Agents—0
Offline Robotic World Model: Learning Robotic Policies without a Physics Simulator—0
Data-Assimilated Model-Based Reinforcement Learning for Partially Observed Chaotic Flows—0
Natural Policy Gradient for Average Reward Non-Stationary RL—0
Hybrid Reinforcement Learning and Model Predictive Control for Adaptive Control of Hydrogen-Diesel Dual-Fuel Combustion—0
Reinforcement learning framework for the mechanical design of microelectronic components under multiphysics constraints—0
TTRL: Test-Time Reinforcement LearningCode7
Tina: Tiny Reasoning Models via LoRACode3
SLiM-Gym: Reinforcement Learning for Population Genetics—0
Policy-Based Radiative Transfer: Solving the 2-Level Atom Non-LTE Problem using Soft Actor-Critic Reinforcement Learning—0
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Benchmark Results

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