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 9476–9500 of 15113 papers

TitleStatusHype
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
Maneuver Decision-Making For Autonomous Air Combat Through Curriculum Learning And Reinforcement Learning With Sparse Rewards—0
MANGA: Method Agnostic Neural-policy Generalization and Adaptation—0
Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability—0
Manifold Regularization for Kernelized LSTD—0
Manipulating Reinforcement Learning: Poisoning Attacks on Cost Signals—0
Manufacturing Dispatching using Reinforcement and Transfer Learning—0
Many Agent Reinforcement Learning Under Partial Observability—0
Many Field Packet Classification with Decomposition and Reinforcement Learning—0
Many-Goals Reinforcement Learning—0
Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systems—0
Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning—0
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Benchmark Results

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