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 4211–4220 of 15113 papers

TitleStatusHype
Adaptive Reinforcement Learning through Evolving Self-Modifying Neural Networks—0
Adaptive Reward-Poisoning Attacks against Reinforcement Learning—0
Adaptive Road Configurations for Improved Autonomous Vehicle-Pedestrian Interactions using Reinforcement Learning—0
Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL—0
Adaptive routing protocols for determining optimal paths in AI multi-agent systems: a priority- and learning-enhanced approach—0
Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols—0
Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization—0
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization—0
Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning—0
Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning—0
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Benchmark Results

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