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 3961–3970 of 15113 papers

TitleStatusHype
Large Language Models are Biased Reinforcement LearnersCode0
Optimal control barrier functions for RL based safe powertrain control—0
Towards Robust Policy: Enhancing Offline Reinforcement Learning with Adversarial Attacks and Defenses—0
Combined film and pulse heating of lithium ion batteries to improve performance in low ambient temperature—0
LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions—0
Stochastic Q-learning for Large Discrete Action Spaces—0
Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning—0
Simulation-Based Benchmarking of Reinforcement Learning Agents for Personalized Retail PromotionsCode0
Fast Two-Time-Scale Stochastic Gradient Method with Applications in Reinforcement Learning—0
Deep Learning in Earthquake Engineering: A Comprehensive Review—0
Show:102550
← PrevPage 397 of 1512Next →

Benchmark Results

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