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 6101–6150 of 15113 papers

TitleStatusHype
Policy Gradient for Reinforcement Learning with General Utilities—0
Square-root regret bounds for continuous-time episodic Markov decision processes—0
MSRL: Distributed Reinforcement Learning with Dataflow Fragments—0
Mastering Spatial Graph Prediction of Road Networks—0
Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation—0
Safe Reinforcement Learning From Pixels Using a Stochastic Latent RepresentationCode0
Robust Bayesian optimization with reinforcement learned acquisition functions—0
Policy Gradients for Probabilistic Constrained Reinforcement Learning—0
EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model—0
GFlowNets and variational inferenceCode0
Learning-Based Adaptive Optimal Control of Linear Time-Delay Systems: A Policy Iteration Approach—0
Comparing BERT-based Reward Functions for Deep Reinforcement Learning in Machine Translation—0
Bayesian Q-learning With Imperfect Expert Demonstrations—0
Can Data Diversity Enhance Learning Generalization?—0
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning—0
Zero-Shot Policy Transfer with Disentangled Task Representation of Meta-Reinforcement Learning—0
RL-MD: A Novel Reinforcement Learning Approach for DNA Motif Discovery—0
Safe Exploration Method for Reinforcement Learning under Existence of DisturbanceCode0
Reward Shaping for User Satisfaction in a REINFORCE Recommender—0
Bounded Robustness in Reinforcement Learning via Lexicographic Objectives—0
Programmable Control of Ultrasound Swarmbots through Reinforcement Learning—0
S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement LearningCode0
Towards a Fully Autonomous UAV Controller for Moving Platform Detection and Landing—0
The Role of Time Delay in Sim2real Transfer of Reinforcement Learning for Cyber-Physical Systems—0
Efficient LSTM Training with Eligibility Traces—0
B2RL: An open-source Dataset for Building Batch Reinforcement LearningCode0
Efficiently Learning Small Policies for Locomotion and Manipulation—0
ASPiRe:Adaptive Skill Priors for Reinforcement Learning—0
Improving Policy Learning via Language Dynamics DistillationCode0
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning—0
Ensemble Reinforcement Learning in Continuous Spaces -- A Hierarchical Multi-Step Approach for Policy Training—0
Contrastive Unsupervised Learning of World Model with Invariant Causal Features—0
How Does Return Distribution in Distributional Reinforcement Learning Help Optimization?—0
Blessing from Human-AI Interaction: Super Reinforcement Learning in Confounded Environments—0
Learning Low-Frequency Motion Control for Robust and Dynamic Robot LocomotionCode0
Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments—0
Learning Parsimonious Dynamics for Generalization in Reinforcement Learning—0
Optimistic MLE -- A Generic Model-based Algorithm for Partially Observable Sequential Decision Making—0
Reinforcement Learning Algorithms: An Overview and Classification—0
Scaling Laws for a Multi-Agent Reinforcement Learning ModelCode0
Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms—0
Online Weighted Q-Ensembles for Reduced Hyperparameter Tuning in Reinforcement Learning—0
Combining Reinforcement Learning and Tensor Networks, with an Application to Dynamical Large DeviationsCode0
Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning—0
Generalization in Deep Reinforcement Learning for Robotic Navigation by Reward Shaping—0
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight GuaranteesCode0
Online Policy Optimization for Robust MDP—0
FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations—0
Guiding Safe Exploration with Weakest Preconditions—0
Disentangling Transfer in Continual Reinforcement Learning—0
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Benchmark Results

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