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 9426–9450 of 15113 papers

TitleStatusHype
Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning—0
Relative Importance Sampling for off-Policy Actor-Critic in Deep Reinforcement Learning—0
Relative Policy-Transition Optimization for Fast Policy Transfer—0
Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning—0
Low-Resource Machine Translation based on Asynchronous Dynamic Programming—0
Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling—0
Low-Thrust Orbital Transfer using Dynamics-Agnostic Reinforcement Learning—0
LPaintB: Learning to Paint from Self-Supervision—0
LPMARL: Linear Programming based Implicit Task Assigment for Hiearchical Multi-Agent Reinforcement Learning—0
LSD-Net: Look, Step and Detect for Joint Navigation and Multi-View Recognition with Deep Reinforcement Learning—0
LSTD with Random Projections—0
LUCIFER: Language Understanding and Context-Infused Framework for Exploration and Behavior Refinement—0
Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control—0
Lyapunov-Based Reinforcement Learning State Estimator—0
Lyapunov-based uncertainty-aware safe reinforcement learning—0
Lyapunov Function Consistent Adaptive Network Signal Control with Back Pressure and Reinforcement Learning—0
Lyapunov Robust Constrained-MDPs: Soft-Constrained Robustly Stable Policy Optimization under Model Uncertainty—0
Lyceum: An efficient and scalable ecosystem for robot learning—0
M3: Mamba-assisted Multi-Circuit Optimization via MBRL with Effective Scheduling—0
M^3RL: Mind-aware Multi-agent Management Reinforcement Learning—0
MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent Reinforcement Learning—0
MACC: Cross-Layer Multi-Agent Congestion Control with Deep Reinforcement Learning—0
Machine Learning aided Crop Yield Optimization—0
Machine learning and control engineering: The model-free case—0
Machine Learning Applications in the Routing in Computer Networks—0
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Benchmark Results

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