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

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Showing 3401–3425 of 15113 papers

TitleStatusHype
Digital Human Interactive Recommendation Decision-Making Based on Reinforcement Learning—0
DeepEdge: A Deep Reinforcement Learning based Task Orchestrator for Edge Computing—0
Beyond Fine-Tuning: Transferring Behavior in Reinforcement Learning—0
Courteous Behavior of Automated Vehicles at Unsignalized Intersections via Reinforcement Learning—0
Agent Environment Cycle Games—0
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces—0
AdaCred: Adaptive Causal Decision Transformers with Feature Crediting—0
AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning—0
A study of first-passage time minimization via Q-learning in heated gridworlds—0
DeepForm: Reasoning Large Language Model for Communication System Formulation—0
Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result—0
DeepGait: Planning and Control of Quadrupedal Gaits using Deep Reinforcement Learning—0
Deep Generative Models with Learnable Knowledge Constraints—0
Deep Graph Convolutional Reinforcement Learning for Financial Portfolio Management -- DeepPocket—0
Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions—0
Deep Hedging of Derivatives Using Reinforcement Learning—0
Deep Hedging with Market Impact—0
Deep Hierarchical Reinforcement Learning Algorithm in Partially Observable Markov Decision Processes—0
Deep Hierarchical Reinforcement Learning Based Recommendations via Multi-goals Abstraction—0
A multi-agent reinforcement learning model of reputation and cooperation in human groups—0
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning—0
Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle—0
Countering Language Drift via Grounding—0
A Study of Continual Learning Methods for Q-Learning—0
A Study of AI Population Dynamics with Million-agent Reinforcement Learning—0
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Benchmark Results

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