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 1801–1825 of 15113 papers

TitleStatusHype
Feasibility Consistent Representation Learning for Safe Reinforcement LearningCode1
Benchmarking Batch Deep Reinforcement Learning AlgorithmsCode1
Automatic Truss Design with Reinforcement LearningCode1
Fast Template Matching and Update for Video Object Tracking and SegmentationCode1
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanismCode1
Fault-Tolerant Federated Reinforcement Learning with Theoretical GuaranteeCode1
ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging ResearchCode1
Automating DBSCAN via Deep Reinforcement LearningCode1
Benchmarking Constraint Inference in Inverse Reinforcement LearningCode1
Hybrid Inverse Reinforcement LearningCode1
Federated Ensemble-Directed Offline Reinforcement LearningCode1
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement LearningCode1
Federated Reinforcement Learning with Environment HeterogeneityCode1
FedFormer: Contextual Federation with Attention in Reinforcement LearningCode1
ImagineBench: Evaluating Reinforcement Learning with Large Language Model RolloutsCode1
Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation ErrorsCode1
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle RoutingCode1
Reinforcement learning with combinatorial actions for coupled restless banditsCode1
Finding Effective Security Strategies through Reinforcement Learning and Self-PlayCode1
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy SearchCode1
Finding Failures in High-Fidelity Simulation using Adaptive Stress Testing and the Backward AlgorithmCode1
Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement LearningCode1
A coevolutionary approach to deep multi-agent reinforcement learningCode1
How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via f-Advantage RegressionCode1
Behavior Proximal Policy OptimizationCode1
Show:102550
← PrevPage 73 of 605Next →

Benchmark Results

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