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 3261–3270 of 15113 papers

TitleStatusHype
Unified Off-Policy Learning to Rank: a Reinforcement Learning PerspectiveCode0
Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care—0
Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement LearningCode0
Multi-market Energy Optimization with Renewables via Reinforcement Learning—0
A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning—0
A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning—0
Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at 100k Steps-Per-SecondCode1
DenseLight: Efficient Control for Large-scale Traffic Signals with Dense FeedbackCode0
Kernelized Reinforcement Learning with Order Optimal Regret Bounds—0
Robust Reinforcement Learning through Efficient Adversarial Herding—0
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Benchmark Results

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