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 4751–4775 of 15113 papers

TitleStatusHype
Safe Exploration Method for Reinforcement Learning under Existence of DisturbanceCode0
Scaling Laws for a Multi-Agent Reinforcement Learning ModelCode0
Online Weighted Q-Ensembles for Reduced Hyperparameter Tuning in Reinforcement Learning—0
Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments—0
Blessing from Human-AI Interaction: Super Reinforcement Learning in Confounded Environments—0
Ensemble Reinforcement Learning in Continuous Spaces -- A Hierarchical Multi-Step Approach for Policy Training—0
Learning Low-Frequency Motion Control for Robust and Dynamic Robot LocomotionCode0
Does Zero-Shot Reinforcement Learning Exist?Code1
Contrastive Unsupervised Learning of World Model with Invariant Causal Features—0
How Does Return Distribution in Distributional Reinforcement Learning Help Optimization?—0
Learning Parsimonious Dynamics for Generalization in Reinforcement Learning—0
Optimistic MLE -- A Generic Model-based Algorithm for Partially Observable Sequential Decision Making—0
Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms—0
Reinforcement Learning Algorithms: An Overview and Classification—0
Offline Reinforcement Learning via High-Fidelity Generative Behavior ModelingCode1
Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning—0
Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement LearningCode2
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight GuaranteesCode0
Generalization in Deep Reinforcement Learning for Robotic Navigation by Reward Shaping—0
Combining Reinforcement Learning and Tensor Networks, with an Application to Dynamical Large DeviationsCode0
Online Policy Optimization for Robust MDP—0
FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations—0
Disentangling Transfer in Continual Reinforcement Learning—0
Argumentative Reward Learning: Reasoning About Human Preferences—0
A simple but strong baseline for online continual learning: Repeated Augmented RehearsalCode1
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Benchmark Results

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