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 731–740 of 15113 papers

TitleStatusHype
Actor-Critic Reinforcement Learning for Control with Stability GuaranteeCode1
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learningCode1
A Game-Theoretic Approach to Multi-Agent Trust Region OptimizationCode1
DeepMind Lab2DCode1
Accelerating lifelong reinforcement learning via reshaping rewardsCode1
Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet DetectionCode1
Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as ObservationCode1
Deep Reinforcement Learning Control of Quantum CartpolesCode1
Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning ApproachCode1
Adversarial Policies: Attacking Deep Reinforcement LearningCode1
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Benchmark Results

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