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 591–600 of 15113 papers

TitleStatusHype
Continual Backprop: Stochastic Gradient Descent with Persistent RandomnessCode1
Continual Model-Based Reinforcement Learning with HypernetworksCode1
Adversarially Trained Actor Critic for Offline Reinforcement LearningCode1
6GAN: IPv6 Multi-Pattern Target Generation via Generative Adversarial Nets with Reinforcement LearningCode1
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable EnvironmentCode1
Continual Reinforcement Learning with Multi-Timescale ReplayCode1
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
Curious Hierarchical Actor-Critic Reinforcement LearningCode1
Deep Reinforcement Learning for Active Human Pose EstimationCode1
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Benchmark Results

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