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 10526–10550 of 15113 papers

TitleStatusHype
Equivalence of Optimality Criteria for Markov Decision Process and Model Predictive Control—0
Equivariant Action Sampling for Reinforcement Learning and Planning—0
Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning—0
Equivariant MuZero—0
Equivariant Offline Reinforcement Learning—0
Equivariant Reinforcement Learning for Quadrotor UAV—0
Ergodic Annealing—0
Error Bounds of Imitating Policies and Environments—0
Error Controlled Actor-Critic Method to Reinforcement Learning—0
Error-related Potential driven Reinforcement Learning for adaptive Brain-Computer Interfaces—0
Escape Room: A Configurable Testbed for Hierarchical Reinforcement Learning—0
Escaping from Zero Gradient: Revisiting Action-Constrained Reinforcement Learning via Frank-Wolfe Policy Optimization—0
ES Is More Than Just a Traditional Finite-Difference Approximator—0
Estimating Link Flows in Road Networks with Synthetic Trajectory Data Generation: Reinforcement Learning-based Approaches—0
Estimating player completion rate in mobile puzzle games using reinforcement learning—0
Estimating scale-invariant future in continuous time—0
ETHER: Aligning Emergent Communication for Hindsight Experience Replay—0
EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model—0
Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach—0
Evading Web Application Firewalls with Reinforcement Learning—0
Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors—0
Evaluating Generalisation in General Video Game Playing—0
Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios—0
Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks—0
Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents—0
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Benchmark Results

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