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 4361–4370 of 15113 papers

TitleStatusHype
Adversarial Reinforcement Learning for Observer Design in Autonomous Systems under Cyber Attacks—0
Adversarial Reinforcement Learning for Procedural Content Generation—0
Adversarial Reinforcement Learning Framework for Benchmarking Collision Avoidance Mechanisms in Autonomous Vehicles—0
Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control—0
Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence—0
Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness—0
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems—0
Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization—0
Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning—0
Adversarial Style Transfer for Robust Policy Optimization in Reinforcement Learning—0
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Benchmark Results

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