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 651–660 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning in Parameterized Action SpaceCode1
Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed TrafficCode1
Deep Policies for Online Bipartite Matching: A Reinforcement Learning ApproachCode1
DeepMind Control SuiteCode1
Sample Efficient Reinforcement Learning via Large Vision Language Model DistillationCode1
DeepMind Lab2DCode1
Faster Deep Reinforcement Learning with Slower Online NetworkCode1
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent SpaceCode1
Active Reinforcement Learning for Robust Building ControlCode1
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement LearningCode1
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Benchmark Results

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