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 3511–3520 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning and the Deadly Triad—0
Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning—0
Deep Reinforcement Learning: An Overview—0
Deep Reinforcement Learning Approach for Trading Automation in The Stock Market—0
Deep reinforcement learning approach to MIMO precoding problem: Optimality and Robustness—0
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT—0
Accelerating the Computation of UCB and Related Indices for Reinforcement Learning—0
DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO—0
Defense Against Reward Poisoning Attacks in Reinforcement Learning—0
A Stochastic Composite Augmented Lagrangian Method For Reinforcement Learning—0
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Benchmark Results

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