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 6326–6350 of 15113 papers

TitleStatusHype
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
Show:102550
← PrevPage 254 of 605Next →

Benchmark Results

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