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

Showing 11111120 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-CommunicationCode1
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam SearchCode1
A2C is a special case of PPOCode1
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement LearningCode1
Reachability Constrained Reinforcement LearningCode1
The Primacy Bias in Deep Reinforcement LearningCode1
Deep Reinforcement Learning for Computational Fluid Dynamics on HPC SystemsCode1
Intelligent Reflecting Surface Configurations for Smart Radio Using Deep Reinforcement LearningCode1
Gamma and Vega Hedging Using Deep Distributional Reinforcement LearningCode1
Efficient Risk-Averse Reinforcement LearningCode1
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Benchmark Results

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