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 12701–12750 of 15113 papers

TitleStatusHype
Control Regularization for Reduced Variance Reinforcement LearningCode0
Combining Parametric and Nonparametric Models for Off-Policy Evaluation—0
Deep Multi-Agent Reinforcement Learning Based Cooperative Edge Caching in Wireless Networks—0
Learning and Exploiting Multiple Subgoals for Fast Exploration in Hierarchical Reinforcement Learning—0
Distributional Reinforcement Learning for Efficient Exploration—0
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic ScenarioCode0
Task-Agnostic Dynamics Priors for Deep Reinforcement LearningCode0
Multi-Agent Image Classification via Reinforcement LearningCode0
Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations—0
Diagnosing Reinforcement Learning for Traffic Signal Control—0
Learning Phase Competition for Traffic Signal ControlCode0
Graph Attention Memory for Visual Navigation—0
Optimizing Routerless Network-on-Chip Designs: An Innovative Learning-Based Framework—0
Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action SpacesCode0
On the Detection of Mutual Influences and Their Consideration in Reinforcement Learning Processes—0
Reinforcement Learning in Non-Stationary Environments—0
Attention-based Deep Reinforcement Learning for Multi-view Environments—0
Domain Adversarial Reinforcement Learning for Partial Domain Adaptation—0
Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement LearningCode0
Intelligent User Association for Symbiotic Radio Networks using Deep Reinforcement Learning—0
Design of Artificial Intelligence Agents for Games using Deep Reinforcement Learning—0
Do Autonomous Agents Benefit from Hearing?—0
Emergent Escape-based Flocking Behavior using Multi-Agent Reinforcement Learning—0
GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing—0
A Reinforcement Learning Perspective on the Optimal Control of Mutation Probabilities for the (1+1) Evolutionary Algorithm: First Results on the OneMax Problem—0
Path Design for Cellular-Connected UAV with Reinforcement Learning—0
Pretrain Soft Q-Learning with Imperfect Demonstrations—0
Toward Packet Routing with Fully-distributed Multi-agent Deep Reinforcement Learning—0
Learning to EvolveCode0
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement LearningCode0
Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning—0
Accelerated Target Updates for Q-learning—0
Continual and Multi-task Reinforcement Learning With Shared Episodic Memory—0
A Complementary Learning Systems Approach to Temporal Difference Learning—0
Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs—0
Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement LearningCode0
Object Exchangeability in Reinforcement Learning: Extended Abstract—0
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving—0
Deep Ordinal Reinforcement LearningCode0
Learning to Control in Metric Space with Optimal RegretCode0
The Game of Tetris in Machine LearningCode0
P3O: Policy-on Policy-off Policy OptimizationCode0
Hierarchical Policy Learning is Sensitive to Goal Space Design—0
A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications—0
Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning—0
Deep Residual Reinforcement Learning—0
Meta-learners' learning dynamics are unlike learners'—0
Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach—0
Adaptive Intelligent Secondary Control of Microgrids Using a Biologically-Inspired Reinforcement Learning—0
Collaborative Evolutionary Reinforcement LearningCode0
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Benchmark Results

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