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 11476–11500 of 15113 papers

TitleStatusHype
Hybrid Supervised and Reinforcement Learning for the Design and Optimization of Nanophotonic Structures—0
Hybrid Systems Neural Control with Region-of-Attraction Planner—0
Mixed Traffic Control and Coordination from Pixels—0
Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation—0
Hybrid UAV-enabled Secure Offloading via Deep Reinforcement Learning—0
Hybrid Value Estimation for Off-policy Evaluation and Offline Reinforcement Learning—0
Hybrid Zero Dynamics Inspired Feedback Control Policy Design for 3D Bipedal Locomotion using Reinforcement Learning—0
Hyperbolically-Discounted Reinforcement Learning on Reward-Punishment Framework—0
Hyperbolic Deep Reinforcement Learning—0
Hyperbolic Embeddings for Learning Options in Hierarchical Reinforcement Learning—0
Hyper: Hyperparameter Robust Efficient Exploration in Reinforcement Learning—0
HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem—0
Hypernetwork Dismantling via Deep Reinforcement Learning—0
Hypernetworks for Zero-shot Transfer in Reinforcement Learning—0
Hyper-parameter Optimisation of Gaussian Process Reinforcement Learning for Statistical Dialogue Management—0
Hyper-parameter optimization based on soft actor critic and hierarchical mixture regularization—0
Hyperparameter Optimization for Multi-Objective Reinforcement Learning—0
Hyperparameter Selection for Offline Reinforcement Learning—0
Hyperparameters in Reinforcement Learning and How To Tune Them—0
Hyperparameter Tuning for Deep Reinforcement Learning Applications—0
Hyperspace Neighbor Penetration Approach to Dynamic Programming for Model-Based Reinforcement Learning Problems with Slowly Changing Variables in A Continuous State Space—0
Hyperspherical Normalization for Scalable Deep Reinforcement Learning—0
Hypothesis Driven Coordinate Ascent for Reinforcement Learning—0
IA-MARL: Imputation Assisted Multi-Agent Reinforcement Learning for Missing Training Data—0
I am Robot: Neuromuscular Reinforcement Learning to Actuate Human Limbs through Functional Electrical Stimulation—0
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Benchmark Results

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