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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6301–6350 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
Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic EnvironmentsCode1
DearFSAC: An Approach to Optimizing Unreliable Federated Learning via Deep Reinforcement Learning—0
Communication-Efficient Consensus Mechanism for Federated Reinforcement Learning—0
Contrastive Learning from Demonstrations—0
Generalized Global Ranking-Aware Neural Architecture Ranker for Efficient Image Classifier SearchCode1
ApolloRL: a Reinforcement Learning Platform for Autonomous Driving—0
DeepRNG: Towards Deep Reinforcement Learning-Assisted Generative Testing of Software—0
Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point ProcessesCode0
Explaining Reinforcement Learning Policies through Counterfactual TrajectoriesCode0
Zeroth-Order Actor-Critic: An Evolutionary Framework for Sequential Decision ProblemsCode0
Discovering Exfiltration Paths Using Reinforcement Learning with Attack Graphs—0
Joint Differentiable Optimization and Verification for Certified Reinforcement Learning—0
Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation—0
A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise—0
FCMNet: Full Communication Memory Net for Team-Level Cooperation in Multi-Agent SystemsCode0
Constrained Variational Policy Optimization for Safe Reinforcement LearningCode1
Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methodsCode0
Can Wikipedia Help Offline Reinforcement Learning?Code1
Dynamic Temporal Reconciliation by Reinforcement learning—0
Towards Safe Reinforcement Learning with a Safety Editor PolicyCode1
Mask-based Latent Reconstruction for Reinforcement LearningCode1
Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints—0
Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments from Temporal Logic Specifications—0
Boosting Exploration in Multi-Task Reinforcement Learning using Adversarial NetworksCode0
The Challenges of Exploration for Offline Reinforcement Learning—0
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Benchmark Results

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