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 8776–8800 of 15113 papers

TitleStatusHype
How RL Agents Behave When Their Actions Are ModifiedCode0
Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement LearningCode1
Does the Adam Optimizer Exacerbate Catastrophic Forgetting?Code0
Cooperation and Reputation Dynamics with Reinforcement Learning—0
ScrofaZero: Mastering Trick-taking Poker Game Gongzhu by Deep Reinforcement LearningCode0
Scaling Multi-Agent Reinforcement Learning with Selective Parameter SharingCode1
Seeing by haptic glance: reinforcement learning-based 3D object Recognition—0
Sparse Attention Guided Dynamic Value Estimation for Single-Task Multi-Scene Reinforcement Learning—0
Reinforcement Learning for IoT Security: A Comprehensive Survey—0
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning—0
Model-free Representation Learning and Exploration in Low-rank MDPs—0
Domain Adversarial Reinforcement Learning—0
A Reinforcement learning method for Optical Thin-Film Design—0
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning—0
Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Network—0
Interactive Learning from Activity DescriptionCode0
LTL2Action: Generalizing LTL Instructions for Multi-Task RLCode1
PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators—0
Modelling Cooperation in Network Games with Spatio-Temporal Complexity—0
Q-Value Weighted Regression: Reinforcement Learning with Limited DataCode0
Reinforcement Learning For Data Poisoning on Graph Neural Networks—0
Discovery of Options via Meta-Learned Subgoals—0
Disturbing Reinforcement Learning Agents with Corrupted Rewards—0
Deep Reinforcement Learning for Backup Strategies against Adversaries—0
Scalable Bayesian Inverse Reinforcement LearningCode1
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Benchmark Results

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