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 5301–5325 of 15113 papers

TitleStatusHype
GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning—0
EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System—0
Humans are not Boltzmann Distributions: Challenges and Opportunities for Modelling Human Feedback and Interaction in Reinforcement Learning—0
Interpretable Hidden Markov Model-Based Deep Reinforcement Learning Hierarchical Framework for Predictive Maintenance of Turbofan Engines—0
Distinguishing Learning Rules with Brain Machine InterfacesCode0
On the Complexity of Adversarial Decision Making—0
When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement LearningCode1
Predicting the Need for Blood Transfusion in Intensive Care Units with Reinforcement Learning—0
Tackling Asymmetric and Circular Sequential Social Dilemmas with Reinforcement Learning and Graph-based Tit-for-TatCode0
Improving Policy Optimization with Generalist-Specialist LearningCode0
Analysis of Stochastic Processes through Replay Buffers—0
Estimating Link Flows in Road Networks with Synthetic Trajectory Data Generation: Reinforcement Learning-based Approaches—0
Functional Optimization Reinforcement Learning for Real-Time Bidding—0
Guided Exploration in Reinforcement Learning via Monte Carlo Critic OptimizationCode0
Hierarchical Reinforcement Learning with Opponent Modeling for Distributed Multi-agent Cooperation—0
Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning—0
Towards Modern Card Games with Large-Scale Action Spaces Through Action Representation—0
Value Function Decomposition for Iterative Design of Reinforcement Learning Agents—0
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems—0
Reinforcement learning based adaptive metaheuristicsCode0
Modeling Adaptive Platoon and Reservation Based Autonomous Intersection Control: A Deep Reinforcement Learning Approach—0
Joint Representation Training in Sequential Tasks with Shared Structure—0
Dynamic network congestion pricing based on deep reinforcement learning—0
Learning the policy for mixed electric platoon control of automated and human-driven vehicles at signalized intersection: a random search approach—0
Eco-driving for Electric Connected Vehicles at Signalized Intersections: A Parameterized Reinforcement Learning approach—0
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Benchmark Results

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