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

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Showing 3176–3200 of 15113 papers

TitleStatusHype
Conservative and Risk-Aware Offline Multi-Agent Reinforcement LearningCode0
Homogenization of Multi-agent Learning Dynamics in Finite-state Markov GamesCode0
How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control?Code0
H_ Model-free Reinforcement Learning with Robust Stability GuaranteeCode0
Hindsight Trust Region Policy OptimizationCode0
Hint assisted reinforcement learning: an application in radio astronomyCode0
MASAI: Multi-agent Summative Assessment Improvement for Unsupervised Environment DesignCode0
HOList: An Environment for Machine Learning of Higher-Order Theorem ProvingCode0
Hindsight Learning for MDPs with Exogenous InputsCode0
Hindsight Foresight Relabeling for Meta-Reinforcement LearningCode0
Massively Parallel Methods for Deep Reinforcement LearningCode0
Hindsight policy gradientsCode0
Conjugated Discrete Distributions for Distributional Reinforcement LearningCode0
Hindsight Credit AssignmentCode0
Highway Graph to Accelerate Reinforcement LearningCode0
Hierarchical Text Generation and Planning for Strategic DialogueCode0
d3rlpy: An Offline Deep Reinforcement Learning LibraryCode0
Hierarchical Reinforcement Learning with the MAXQ Value Function DecompositionCode0
Active Policy Improvement from Multiple Black-box OraclesCode0
Confidence Aware Inverse Constrained Reinforcement LearningCode0
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary RewardsCode0
Hierarchical Reinforcement Learning via Advantage-Weighted Information MaximizationCode0
Hierarchical Reinforcement Learning with Optimal Level Synchronization based on a Deep Generative ModelCode0
High-Throughput Distributed Reinforcement Learning via Adaptive Policy SynchronizationCode0
Hierarchical Object Detection with Deep Reinforcement LearningCode0
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Benchmark Results

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