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 6526–6550 of 15113 papers

TitleStatusHype
An Efficient Combinatorial Optimization Model Using Learning-to-Rank DistillationCode0
Rediscovering Affordance: A Reinforcement Learning Perspective—0
On the Unreasonable Efficiency of State Space Clustering in Personalization TasksCode0
Learning to Walk with Dual Agents for Knowledge Graph ReasoningCode1
Improving the Efficiency of Off-Policy Reinforcement Learning by Accounting for Past Decisions—0
Missing Velocity in Dynamic Obstacle Avoidance based on Deep Reinforcement Learning—0
Safety and Liveness Guarantees through Reach-Avoid Reinforcement LearningCode1
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning—0
Alpha-Mini: Minichess Agent with Deep Reinforcement LearningCode0
Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving PoliciesCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Graph Information—0
Graph augmented Deep Reinforcement Learning in the GameRLand3D environment—0
Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-agent Urban Driving EnvironmentCode0
Direct Behavior Specification via Constrained Reinforcement LearningCode1
Maximum Entropy Population-Based Training for Zero-Shot Human-AI CoordinationCode1
Newsvendor Model with Deep Reinforcement LearningCode0
Reinforcement Learning based Sequential Batch-sampling for Bayesian Optimal Experimental Design—0
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee—0
Off Environment Evaluation Using Convex Risk MinimizationCode0
Soft Actor-Critic with Cross-Entropy Policy OptimizationCode0
District Cooling System Control for Providing Operating Reserve based on Safe Deep Reinforcement Learning—0
Do Androids Dream of Electric Fences? Safety-Aware Reinforcement Learning with Latent Shielding—0
Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach—0
A Scalable Deep Reinforcement Learning Model for Online Scheduling Coflows of Multi-Stage Jobs for High Performance Computing—0
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Benchmark Results

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