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 2491–2500 of 15113 papers

TitleStatusHype
Avoiding Negative Side-Effects and Promoting Safe Exploration with Imaginative Planning—0
Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning—0
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures—0
Avoiding Jammers: A Reinforcement Learning Approach—0
Avoiding Catastrophic States with Intrinsic Fear—0
Adaptive Reinforcement Learning for Unobservable Random Delays—0
A Benchmarking Environment for Reinforcement Learning Based Task Oriented Dialogue Management—0
Continuous Input Embedding Size Search For Recommender Systems—0
Avoidance Learning Using Observational Reinforcement Learning—0
A Visual Communication Map for Multi-Agent Deep Reinforcement Learning—0
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Benchmark Results

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