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 8601–8650 of 15113 papers

TitleStatusHype
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation—0
Provably Efficient CVaR RL in Low-rank MDPs—0
Provably Efficient Exploration in Policy Optimization—0
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret—0
Provably Efficient Exploration in Reward Machines with Low Regret—0
Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback—0
Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximation—0
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs—0
Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication—0
Provably Efficient Multi-Task Reinforcement Learning with Model Transfer—0
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus—0
Provably Efficient Offline Reinforcement Learning with Trajectory-Wise Reward—0
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources—0
Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints—0
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle—0
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle—0
Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces—0
Provably Efficient Reinforcement Learning with Aggregated States—0
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension—0
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations—0
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints—0
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping—0
Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization—0
Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games—0
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems—0
Provably Efficient Reinforcement Learning via Surprise Bound—0
Provably Efficient Representation Selection in Low-rank Markov Decision Processes: From Online to Offline RL—0
Provably Efficient RL under Episode-Wise Safety in Constrained MDPs with Linear Function Approximation—0
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning—0
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics—0
Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration—0
Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments—0
Provably Safe Model-Based Meta Reinforcement Learning: An Abstraction-Based Approach—0
Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking—0
Provably Safe Reinforcement Learning via Action Projection using Reachability Analysis and Polynomial Zonotopes—0
Provably Sample-Efficient RL with Side Information about Latent Dynamics—0
Proximal Bellman mappings for reinforcement learning and their application to robust adaptive filtering—0
Proximal Deterministic Policy Gradient—0
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning—0
Proximal Policy Optimization and its Dynamic Version for Sequence Generation—0
Proximal Policy Optimization-Based Reinforcement Learning Approach for DC-DC Boost Converter Control: A Comparative Evaluation Against Traditional Control Techniques—0
Proximal Policy Optimization for Tracking Control Exploiting Future Reference Information—0
Proximal Policy Optimization via Enhanced Exploration Efficiency—0
Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces—0
Proximal Reliability Optimization for Reinforcement Learning—0
Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning—0
Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model with Proxy—0
Proxy Target: Bridging the Gap Between Discrete Spiking Neural Networks and Continuous Control—0
PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets—0
Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care—0
Show:102550
← PrevPage 173 of 303Next →

Benchmark Results

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