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 6301–6325 of 15113 papers

TitleStatusHype
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization—0
Federated Reinforcement Learning for Collective Navigation of Robotic Swarms—0
Transfer in Reinforcement Learning via Regret Bounds for Learning Agents—0
Optimizing Sequential Experimental Design with Deep Reinforcement LearningCode1
Tutorial on amortized optimizationCode2
Sequential Search with Off-Policy Reinforcement Learning—0
Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning—0
Reinforcement learning of optimal active particle navigation—0
CIC: Contrastive Intrinsic Control for Unsupervised Skill DiscoveryCode1
Distributional Reinforcement Learning with Regularized Wasserstein LossCode0
A General, Evolution-Inspired Reward Function for Social RoboticsCode0
Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary StrategiesCode1
Improving Sample Efficiency of Value Based Models Using Attention and Vision Transformers—0
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning ApproachCode1
Reinforcement Learning with Heterogeneous Data: Estimation and Inference—0
On solutions of the distributional Bellman equation—0
Steady-State Error Compensation in Reference Tracking and Disturbance Rejection Problems for Reinforcement Learning-Based ControlCode0
Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback—0
Warmth and competence in human-agent cooperation—0
Cooperative Online Learning in Stochastic and Adversarial MDPs—0
Compositional Multi-Object Reinforcement Learning with Linear Relation Networks—0
Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement LearningCode1
DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement LearningCode0
Score vs. Winrate in Score-Based Games: which Reward for Reinforcement Learning?—0
Coordinated Frequency Control through Safe Reinforcement Learning—0
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Benchmark Results

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