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 2751–2760 of 15113 papers

TitleStatusHype
Imagination-Augmented Hierarchical Reinforcement Learning for Safe and Interactive Autonomous Driving in Urban Environments—0
Data-Driven LQR using Reinforcement Learning and Quadratic Neural Networks—0
Runtime Verification of Learning Properties for Reinforcement Learning Algorithms—0
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
Reinforcement Learning with Model Predictive Control for Highway Ramp MeteringCode1
Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk DecodingCode1
On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling—0
Purpose in the Machine: Do Traffic Simulators Produce Distributionally Equivalent Outcomes for Reinforcement Learning Applications?—0
Adversarial Imitation Learning On Aggregated Data—0
Workflow-Guided Response Generation for Task-Oriented Dialogue—0
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Benchmark Results

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