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 11476–11500 of 15113 papers

TitleStatusHype
Decentralized Automotive Radar Spectrum Allocation to Avoid Mutual Interference Using Reinforcement Learning—0
Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar—0
High-speed Autonomous Drifting with Deep Reinforcement Learning—0
Learning Reusable Options for Multi-Task Reinforcement Learning—0
Generalizing Emergent Communication—0
Optimal Options for Multi-Task Reinforcement Learning Under Time Constraints—0
Universal Successor Features for Transfer Reinforcement Learning—0
Hierarchical Reinforcement Learning as a Model of Human Task Interleaving—0
Intelligent Roundabout Insertion using Deep Reinforcement Learning—0
Making Sense of Reinforcement Learning and Probabilistic Inference—0
Zero-Shot Reinforcement Learning with Deep Attention Convolutional Neural Networks—0
Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics—0
Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation—0
Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate—0
Learning General-Purpose Controllers via Locally Communicating Sensorimotor Modules—0
Deep Randomized Least Squares Value Iteration—0
Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards—0
Batch Reinforcement Learning with Hyperparameter Gradients—0
Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach—0
A Game Theoretic Perspective on Model-Based Reinforcement Learning—0
CoMic: Co-Training and Mimicry for Reusable Skills—0
Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning—0
A distributional view on multi objective policy optimization—0
Inductive Bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters—0
Learning Representations in Reinforcement Learning: an Information Bottleneck Approach—0
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Benchmark Results

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