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 3451–3460 of 15113 papers

TitleStatusHype
Reinforcement Learning with Partial Parametric Model Knowledge—0
Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution TrajectoriesCode0
Multi-criteria Hardware Trojan Detection: A Reinforcement Learning Approach—0
Model Extraction Attacks Against Reinforcement Learning Based Controllers—0
A Closer Look at Reward Decomposition for High-Level Robotic Explanations—0
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
What can online reinforcement learning with function approximation benefit from general coverage conditions?—0
Loss- and Reward-Weighting for Efficient Distributed Reinforcement Learning—0
Proximal Curriculum for Reinforcement Learning AgentsCode0
On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes—0
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Benchmark Results

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