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 4491–4500 of 15113 papers

TitleStatusHype
A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning—0
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy—0
Replay across Experiments: A Natural Extension of Off-Policy RL—0
Optimal Observer Design Using Reinforcement Learning and Quadratic Neural Networks—0
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation—0
Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement LearningCode0
Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline Reinforcement Learning—0
Margin Trader: A Reinforcement Learning Framework for Portfolio Management with Margin and ConstraintsCode0
Digital Twin-Native AI-Driven Service Architecture for Industrial Networks—0
Evaluating Pretrained models for Deployable Lifelong Learning—0
Show:102550
← PrevPage 450 of 1512Next →

Benchmark Results

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