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 2676–2700 of 15113 papers

TitleStatusHype
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
Training Large Language Models to Reason via EM Policy Gradient—0
Reinforcement learning framework for the mechanical design of microelectronic components under multiphysics constraints—0
Data-Assimilated Model-Based Reinforcement Learning for Partially Observed Chaotic Flows—0
Offline Robotic World Model: Learning Robotic Policies without a Physics Simulator—0
Natural Policy Gradient for Average Reward Non-Stationary RL—0
Monte Carlo Planning with Large Language Model for Text-Based Game Agents—0
Hybrid Reinforcement Learning and Model Predictive Control for Adaptive Control of Hydrogen-Diesel Dual-Fuel Combustion—0
Insights from Verification: Training a Verilog Generation LLM with Reinforcement Learning with Testbench Feedback—0
Real-Time Optimal Design of Experiment for Parameter Identification of Li-Ion Cell Electrochemical Model—0
StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream Generation—0
SARI: Structured Audio Reasoning via Curriculum-Guided Reinforcement Learning—0
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
LAPP: Large Language Model Feedback for Preference-Driven Reinforcement Learning—0
Dynamic Contrastive Skill Learning with State-Transition Based Skill Clustering and Dynamic Length Adjustment—0
Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL—0
OTC: Optimal Tool Calls via Reinforcement Learning—0
Relation-R1: Cognitive Chain-of-Thought Guided Reinforcement Learning for Unified Relational Comprehension—0
Mixed-Precision Conjugate Gradient Solvers with RL-Driven Precision Tuning—0
Quantum-Enhanced Reinforcement Learning for Power Grid Security Assessment—0
Unlearning Works Better Than You Think: Local Reinforcement-Based Selection of Auxiliary Objectives—0
Improving RL Exploration for LLM Reasoning through Retrospective Replay—0
SwitchMT: An Adaptive Context Switching Methodology for Scalable Multi-Task Learning in Intelligent Autonomous Agents—0
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Benchmark Results

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