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 9226–9250 of 15113 papers

TitleStatusHype
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
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Benchmark Results

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