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

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Showing 511–520 of 15113 papers

TitleStatusHype
DMC-VB: A Benchmark for Representation Learning for Control with Visual DistractorsCode1
Reinforcement Learning-based Model Predictive Control for Greenhouse Climate ControlCode1
Leveraging Symmetry to Accelerate Learning of Trajectory Tracking Controllers for Free-Flying Robotic SystemsCode1
Enhancing RL Safety with Counterfactual LLM ReasoningCode1
AnyBipe: An End-to-End Framework for Training and Deploying Bipedal Robots Guided by Large Language ModelsCode1
Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory controlCode1
What makes math problems hard for reinforcement learning: a case studyCode1
Control-Informed Reinforcement Learning for Chemical ProcessesCode1
Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct OptimizationCode1
Fine-tuning LLMs for Autonomous Spacecraft Control: A Case Study Using Kerbal Space ProgramCode1
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Benchmark Results

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