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 7401–7425 of 15113 papers

TitleStatusHype
MADRaS : Multi Agent Driving Simulator—0
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL—0
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning—0
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network—0
MAGICS: Adversarial RL with Minimax Actors Guided by Implicit Critic Stackelberg for Convergent Neural Synthesis of Robot Safety—0
Magistral—0
Magnetic Field-Based Reward Shaping for Goal-Conditioned Reinforcement Learning—0
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning—0
Maintaining cooperation in complex social dilemmas using deep reinforcement learning—0
Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks—0
Making Curiosity Explicit in Vision-based RL—0
Making Meaning: Semiotics Within Predictive Knowledge Architectures—0
Making Reinforcement Learning Work on Swimmer—0
Making Sense of Reinforcement Learning and Probabilistic Inference—0
Making Smart Homes Smarter: Optimizing Energy Consumption with Human in the Loop—0
Malaria Likelihood Prediction By Effectively Surveying Households Using Deep Reinforcement Learning—0
Malleable Agents for Re-Configurable Robotic Manipulators—0
MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions—0
Malthusian Reinforcement Learning—0
MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning—0
MAMPS: Safe Multi-Agent Reinforcement Learning via Model Predictive Shielding—0
MAMRL: Exploiting Multi-agent Meta Reinforcement Learning in WAN Traffic Engineering—0
Managing caching strategies for stream reasoning with reinforcement learning—0
Managing engineering systems with large state and action spaces through deep reinforcement learning—0
Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off—0
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Benchmark Results

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