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 7251–7300 of 15113 papers

TitleStatusHype
Extendable NFV-Integrated Control Method Using Reinforcement Learning—0
Extend Adversarial Policy Against Neural Machine Translation via Unknown Token—0
Extended Radial Basis Function Controller for Reinforcement Learning—0
Extending a Quantum Reinforcement Learning Exploration Policy with Flags to Connect Four—0
Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making—0
External control of a genetic toggle switch via Reinforcement Learning—0
EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data—0
Extracting Action Sequences from Texts Based on Deep Reinforcement Learning—0
Extracting Expert's Goals by What-if Interpretable Modeling—0
Extracting Latent State Representations with Linear Dynamics from Rich Observations—0
Extrapolation in Gridworld Markov-Decision Processes—0
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory—0
Extreme State Aggregation Beyond MDPs—0
Extreme Value Monte Carlo Tree Search—0
ExWarp: Extrapolation and Warping-based Temporal Supersampling for High-frequency Displays—0
Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agents—0
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning—0
Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning—0
FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots—0
Face valuing: Training user interfaces with facial expressions and reinforcement learning—0
Facial Feedback for Reinforcement Learning: A Case Study and Offline Analysis Using the TAMER Framework—0
Factored Action Spaces in Deep Reinforcement Learning—0
Factored Adaptation for Non-Stationary Reinforcement Learning—0
FactoredRL: Leveraging Factored Graphs for Deep Reinforcement Learning—0
Factor Representation and Decision Making in Stock Markets Using Deep Reinforcement Learning—0
Faded-Experience Trust Region Policy Optimization for Model-Free Power Allocation in Interference Channel—0
Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving—0
Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning—0
FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoT—0
Fair Loss: Margin-Aware Reinforcement Learning for Deep Face Recognition—0
Fairness Incentives in Response to Unfair Dynamic Pricing—0
Fairness in Multi-agent Reinforcement Learning for Stock Trading—0
Fairness in Reinforcement Learning—0
Fairness in Reinforcement Learning—0
Fairness in Reinforcement Learning: A Survey—0
Fake News Mitigation via Point Process Based Intervention—0
Falsification-Based Robust Adversarial Reinforcement Learning—0
Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning—0
Fast active learning for pure exploration in reinforcement learning—0
Fast Adaptation with Behavioral Foundation Models—0
Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction—0
Fast Algorithms for L_-constrained S-rectangular Robust MDPs—0
Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation—0
Fast Approximate Solutions using Reinforcement Learning for Dynamic Capacitated Vehicle Routing with Time Windows—0
Trust-Region Method with Deep Reinforcement Learning in Analog Design Space Exploration—0
Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES—0
Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learning—0
Faster Deep Q-learning using Neural Episodic Control—0
Faster Machine Translation Ensembling with Reinforcement Learning and Competitive Correction—0
Faster Reinforcement Learning with Expert State Sequences—0
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Benchmark Results

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