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 3741–3750 of 15113 papers

TitleStatusHype
Minimax-Bayes Reinforcement LearningCode0
Constrained Reinforcement Learning for Predictive Control in Real-Time Stochastic Dynamic Optimal Power Flow—0
Handling Long and Richly Constrained Tasks through Constrained Hierarchical Reinforcement Learning—0
Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space—0
Reinforcement Learning-based Control of Nonlinear Systems using Carleman Approximation: Structured and Unstructured Designs—0
UAV Path Planning Employing MPC- Reinforcement Learning Method Considering Collision Avoidance—0
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret—0
Multiagent Inverse Reinforcement Learning via Theory of Mind ReasoningCode0
Deep Reinforcement Learning for Cost-Effective Medical DiagnosisCode1
Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue SystemsCode0
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Benchmark Results

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