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 2126–2150 of 15113 papers

TitleStatusHype
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced DatasetsCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
Deceptive Path Planning via Reinforcement Learning with Graph Neural NetworksCode1
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement LearningCode1
Decoupling Strategy and Generation in Negotiation DialoguesCode1
Decoupling Value and Policy for Generalization in Reinforcement LearningCode1
Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile NetworksCode1
Deep Black-Box Reinforcement Learning with Movement PrimitivesCode1
Deep Intrinsically Motivated Exploration in Continuous ControlCode1
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardsCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
Bidirectional Model-based Policy OptimizationCode1
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy SearchCode1
Blockchain Framework for Artificial Intelligence ComputationCode1
Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation ErrorsCode1
Computational Performance of Deep Reinforcement Learning to find Nash EquilibriaCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Blue River Controls: A toolkit for Reinforcement Learning Control Systems on HardwareCode1
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions ModelingCode1
Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet DetectionCode1
Deep Reinforcement Learning Control of Quantum CartpolesCode1
Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest OverfittingCode1
Deep Reinforcement Learning for Active Human Pose EstimationCode1
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlowCode1
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Benchmark Results

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