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 9351–9375 of 15113 papers

TitleStatusHype
Reinforcement Learning for Robust Missile Autopilot Design—0
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet SpaceCode1
Exploring grid topology reconfiguration using a simple deep reinforcement learning approach—0
Learning from Simulation, Racing in Reality—0
Interactive Machine Learning of Musical GestureCode1
Generalization in Reinforcement Learning by Soft Data AugmentationCode1
MetaSensing: Intelligent Metasurface Assisted RF 3D Sensing by Deep Reinforcement Learning—0
Diluted Near-Optimal Expert Demonstrations for Guiding Dialogue Stochastic Policy Optimisation—0
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement LearningCode0
Auto Graph Encoder-Decoder for Neural Network Pruning—0
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
RLlib Flow: Distributed Reinforcement Learning is a Dataflow ProblemCode4
TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement LearningCode1
Symmetry-Aware Actor-Critic for 3D Molecular DesignCode1
Towards Playing Full MOBA Games with Deep Reinforcement Learning—0
World Model as a Graph: Learning Latent Landmarks for PlanningCode1
PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control—0
REPAINT: Knowledge Transfer in Deep Reinforcement Learning—0
Solving The Lunar Lander Problem under Uncertainty using Reinforcement LearningCode0
Learning Principle of Least Action with Reinforcement LearningCode0
A Reusable Framework Based on Reinforcement Learning to Design Antennas for Curved Surfaces—0
Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs—0
Path Design and Resource Management for NOMA enhanced Indoor Intelligent Robots—0
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation—0
Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games—0
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Benchmark Results

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