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 10276–10300 of 15113 papers

TitleStatusHype
Falsification-Based Robust Adversarial Reinforcement Learning—0
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location—0
Sequential Transfer in Reinforcement Learning with a Generative Model—0
Personalization of Hearing Aid Compression by Human-In-Loop Deep Reinforcement Learning—0
UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning ApproachCode1
Reinforcement Learning based Control of Imitative Policies for Near-Accident DrivingCode1
A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing—0
Interaction-limited Inverse Reinforcement Learning—0
Convex Regularization in Monte-Carlo Tree Search—0
Debiased Contrastive LearningCode1
Developing cooperative policies for multi-stage tasks—0
Composing Elementary Discourse Units in Abstractive Summarization—0
Zero-shot Text Classification via Reinforced Self-training—0
Meta-Reinforced Multi-Domain State Generator for Dialogue Systems—0
Testing match-3 video games with Deep Reinforcement Learning—0
MDP Homomorphic Networks: Group Symmetries in Reinforcement LearningCode1
Model-based Reinforcement Learning: A Survey—0
Enforcing Almost-Sure Reachability in POMDPsCode0
Accelerating Reinforcement Learning Agent with EEG-based Implicit Human Feedback—0
Evaluating the Performance of Reinforcement Learning AlgorithmsCode1
Deep reinforcement learning approach to MIMO precoding problem: Optimality and Robustness—0
Dynamic Regret of Policy Optimization in Non-stationary Environments—0
Deep Feature Space: A Geometrical PerspectiveCode0
Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids—0
Empirically Verifying Hypotheses Using Reinforcement Learning—0
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Benchmark Results

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