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 641–650 of 15113 papers

TitleStatusHype
Actor-Attention-Critic for Multi-Agent Reinforcement LearningCode1
Accelerating Quadratic Optimization with Reinforcement LearningCode1
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent SpaceCode1
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardsCode1
Automatic Curriculum Learning through Value DisagreementCode1
Deep Laplacian-based Options for Temporally-Extended ExplorationCode1
Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed TrafficCode1
Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet DetectionCode1
Deep Reinforcement Learning for Resource Allocation in Business ProcessesCode1
Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile NetworksCode1
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Benchmark Results

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