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 4526–4550 of 15113 papers

TitleStatusHype
Attitude Control of Highly Maneuverable Aircraft Using an Improved Q-learning—0
Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES—0
Probing Transfer in Deep Reinforcement Learning without Task Engineering—0
Towards Quantum-Enabled 6G Slicing—0
Rate-Splitting for Intelligent Reflecting Surface-Aided Multiuser VR StreamingCode0
Epistemic Monte Carlo Tree Search—0
On the connection between Bregman divergence and value in regularized Markov decision processes—0
Implicit Offline Reinforcement Learning via Supervised Learning—0
Continual Vision-based Reinforcement Learning with Group Symmetries—0
Biologically Plausible Variational Policy Gradient with Spiking Recurrent Winner-Take-All NetworksCode0
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables—0
Deep Reinforcement Learning for Stabilization of Large-scale Probabilistic Boolean Networks—0
Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities—0
Deep Reinforcement Learning for Inverse Inorganic Materials Design—0
Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents—0
PaCo: Parameter-Compositional Multi-Task Reinforcement LearningCode1
Fine-Grained Session Recommendations in E-commerce using Deep Reinforcement Learning—0
Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes—0
Robust Imitation via Mirror Descent Inverse Reinforcement Learning—0
Model-based Lifelong Reinforcement Learning with Bayesian ExplorationCode0
MoCoDA: Model-based Counterfactual Data AugmentationCode1
The Pump Scheduling Problem: A Real-World Scenario for Reinforcement LearningCode0
Safe Policy Improvement in Constrained Markov Decision Processes—0
Task Phasing: Automated Curriculum Learning from DemonstrationsCode0
RMBench: Benchmarking Deep Reinforcement Learning for Robotic Manipulator ControlCode1
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Benchmark Results

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