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 10451–10475 of 15113 papers

TitleStatusHype
Control of a Nature-inspired Scorpion using Reinforcement Learning—0
Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems—0
Deep Reinforcement Learning for Contact-Rich Skills Using Compliant Movement Primitives—0
How does the structure embedded in learning policy affect learning quadruped locomotion?—0
Reinforcement Learning with Feedback-modulated TD-STDP—0
Real-world Video Adaptation with Reinforcement Learning—0
Sample Efficiency in Sparse Reinforcement Learning: Or Your Money Back—0
Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles—0
Query Focused Multi-document Summarisation of Biomedical TextsCode0
The Advantage Regret-Matching Actor-Critic—0
Query Focused Multi-document Summarisation of Biomedical Texts: Macquarie Universiy and the Australian National University at BioASQ8bCode0
Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI—0
Controlling Level of Unconsciousness by Titrating Propofol with Deep Reinforcement Learning—0
AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement LearningCode0
Constrained Markov Decision Processes via Backward Value Functions—0
Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon—0
Identifying Critical States by the Action-Based Variance of Expected Return—0
Synthetic Sample Selection via Reinforcement Learning—0
Selective Particle Attention: Visual Feature-Based Attention in Deep Reinforcement Learning—0
t-Soft Update of Target Network for Deep Reinforcement Learning—0
Robust Reinforcement Learning: A Case Study in Linear Quadratic Regulation—0
Ensuring Monotonic Policy Improvement in Entropy-regularized Value-based Reinforcement Learning—0
Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing—0
Dynamic Dispatching for Large-Scale Heterogeneous Fleet via Multi-agent Deep Reinforcement Learning—0
Improved Memories Learning—0
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Benchmark Results

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