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 16511660 of 15113 papers

TitleStatusHype
AI2-THOR: An Interactive 3D Environment for Visual AICode1
Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement LearningCode1
An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement LearningCode1
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
Bayesian Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
Attacking Video Recognition Models with Bullet-Screen CommentsCode1
Online Symbolic Music Alignment with Offline Reinforcement LearningCode1
On Pathologies in KL-Regularized Reinforcement Learning from Expert DemonstrationsCode1
Attention Actor-Critic algorithm for Multi-Agent Constrained Co-operative Reinforcement LearningCode1
Goal-Aware Cross-Entropy for Multi-Target Reinforcement LearningCode1
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Benchmark Results

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