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 1791–1800 of 15113 papers

TitleStatusHype
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal controlCode1
Fast Context Adaptation via Meta-LearningCode1
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement LearningCode1
Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective TrajectoriesCode1
Reinforcement Learning for Dynamic Resource Allocation in Optical Networks: Hype or Hope?Code1
Reinforcement learning for Energies of the future and carbon neutrality: a Challenge DesignCode1
Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand SystemsCode1
Extreme Q-Learning: MaxEnt RL without EntropyCode1
Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation ErrorsCode1
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy SearchCode1
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Benchmark Results

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