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 2671–2680 of 15113 papers

TitleStatusHype
LLMs for Engineering: Teaching Models to Design High Powered Rockets—0
BQSched: A Non-intrusive Scheduler for Batch Concurrent Queries via Reinforcement LearningCode0
LLM-hRIC: LLM-empowered Hierarchical RAN Intelligent Control for O-RAN—0
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
Training Large Language Models to Reason via EM Policy Gradient—0
Integrating Learning-Based Manipulation and Physics-Based Locomotion for Whole-Body Badminton Robot Control—0
SAPO-RL: Sequential Actuator Placement Optimization for Fuselage Assembly via Reinforcement Learning—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
Show:102550
← PrevPage 268 of 1512Next →

Benchmark Results

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