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 10626–10650 of 15113 papers

TitleStatusHype
Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehicle—0
Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognition—0
Meta-Gradient Reinforcement Learning with an Objective Discovered Online—0
Reinforcement Learning-Enabled Decision-Making Strategies for a Vehicle-Cyber-Physical-System in Connected Environment—0
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity—0
Qgraph-bounded Q-learning: Stabilizing Model-Free Off-Policy Deep Reinforcement Learning—0
Computation Offloading in Beyond 5G Networks: A Distributed Learning Framework and Applications—0
Deep PQR: Solving Inverse Reinforcement Learning using Anchor ActionsCode0
Information Freshness-Aware Task Offloading in Air-Ground Integrated Edge Computing Systems—0
Inverse Reinforcement Learning from a Gradient-based Learner—0
Learning to Sample with Local and Global Contexts in Experience Replay Buffer—0
Robustifying Reinforcement Learning Agents via Action Space Adversarial Training—0
Single-partition adaptive Q-learningCode0
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning—0
Revisiting Fundamentals of Experience ReplayCode0
Reinforcement Learning of Musculoskeletal Control from Functional SimulationsCode0
AirCapRL: Autonomous Aerial Human Motion Capture using Deep Reinforcement Learning—0
A Provably Efficient Sample Collection Strategy for Reinforcement Learning—0
Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learning—0
Learning Abstract Models for Strategic Exploration and Fast Reward Transfer—0
Relational-Grid-World: A Novel Relational Reasoning Environment and An Agent Model for Relational Information Extraction—0
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Approaches—0
XCS as a reinforcement learning approach to automatic test case prioritizationCode0
Simulating multi-exit evacuation using deep reinforcement learning—0
Investigation of Sentiment Controllable Chatbot—0
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Benchmark Results

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