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 6126–6150 of 15113 papers

TitleStatusHype
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