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 7326–7350 of 15113 papers

TitleStatusHype
Offline Pre-trained Multi-Agent Decision Transformer—0
Transformers are Meta-Reinforcement Learners—0
Resmax: An Alternative Soft-Greedy Operator for Reinforcement Learning—0
Offline-Online Reinforcement Learning: Extending Batch and Online RL—0
State-Action Joint Regularized Implicit Policy for Offline Reinforcement Learning—0
Pareto Policy Pool for Model-based Offline Reinforcement Learning—0
Understanding and Leveraging Overparameterization in Recursive Value Estimation—0
Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning—0
Deep Inverse Reinforcement Learning via Adversarial One-Class Classification—0
IA-MARL: Imputation Assisted Multi-Agent Reinforcement Learning for Missing Training Data—0
Joint Self-Supervised Learning for Vision-based Reinforcement Learning—0
Data Sharing without Rewards in Multi-Task Offline Reinforcement Learning—0
Closed-Loop Control of Additive Manufacturing via Reinforcement Learning—0
Distributional Reinforcement Learning with Monotonic Splines—0
DiBB: Distributing Black-Box Optimization—0
Distributional Decision Transformer for Hindsight Information Matching—0
Learning When and What to Ask: a Hierarchical Reinforcement Learning Framework—0
An Experimental Design Perspective on Exploration in Reinforcement Learning—0
EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning—0
Generalizing Successor Features to continuous domains for Multi-task Learning—0
Decentralized Cross-Entropy Method for Model-Based Reinforcement Learning—0
Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward—0
Bi-linear Value Networks for Multi-goal Reinforcement Learning—0
Learning Pseudometric-based Action Representations for Offline Reinforcement Learning—0
Evaluating Robustness of Cooperative MARL—0
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Benchmark Results

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