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 2671–2680 of 15113 papers

TitleStatusHype
PerfRL: A Small Language Model Framework for Efficient Code Optimization—0
Guaranteed Trust Region Optimization via Two-Phase KL Penalization—0
Exploring Parity Challenges in Reinforcement Learning through Curriculum Learning with Noisy LabelsCode0
UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal ControlCode1
Modeling Risk in Reinforcement Learning: A Literature Mapping—0
Multi-Agent Reinforcement Learning via Distributed MPC as a Function ApproximatorCode1
Reinforcement Learning-Based Bionic Reflex Control for Anthropomorphic Robotic Grasping exploiting Domain Randomization—0
Efficient Parallel Reinforcement Learning Framework using the Reactor ModelCode0
MICRO: Model-Based Offline Reinforcement Learning with a Conservative Bellman OperatorCode0
CODEX: A Cluster-Based Method for Explainable Reinforcement LearningCode0
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Benchmark Results

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