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 1501–1510 of 15113 papers

TitleStatusHype
AdaMemento: Adaptive Memory-Assisted Policy Optimization for Reinforcement Learning—0
A Reinforcement Learning Engine with Reduced Action and State Space for Scalable Cyber-Physical Optimal Response—0
DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL—0
Improving Portfolio Optimization Results with Bandit NetworksCode0
Spatial-aware decision-making with ring attractors in reinforcement learning systems—0
Predictive Coding for Decision TransformerCode1
CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character controlCode3
Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector QuantizationCode1
Solving Reach-Avoid-Stay Problems Using Deep Deterministic Policy Gradients—0
Efficient Residual Learning with Mixture-of-Experts for Universal Dexterous Grasping—0
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Benchmark Results

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