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 7426–7450 of 15113 papers

TitleStatusHype
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning—0
Challenging Common Assumptions in Convex Reinforcement Learning—0
Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems—0
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization—0
Federated Reinforcement Learning for Collective Navigation of Robotic Swarms—0
Improved Regret for Differentially Private Exploration in Linear MDP—0
Transfer in Reinforcement Learning via Regret Bounds for Learning Agents—0
Reinforcement learning of optimal active particle navigation—0
Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning—0
Sequential Search with Off-Policy Reinforcement Learning—0
A General, Evolution-Inspired Reward Function for Social RoboticsCode0
Improving Sample Efficiency of Value Based Models Using Attention and Vision Transformers—0
Distributional Reinforcement Learning with Regularized Wasserstein LossCode0
DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement LearningCode0
Cooperative Online Learning in Stochastic and Adversarial MDPs—0
Score vs. Winrate in Score-Based Games: which Reward for Reinforcement Learning?—0
Compositional Multi-Object Reinforcement Learning with Linear Relation Networks—0
Warmth and competence in human-agent cooperation—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
Reinforcement Learning with Heterogeneous Data: Estimation and Inference—0
Communication-Efficient Consensus Mechanism for Federated Reinforcement Learning—0
Contrastive Learning from Demonstrations—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