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 9951–9975 of 15113 papers

TitleStatusHype
Non-Crossing Quantile Regression for Distributional Reinforcement Learning—0
Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration—0
MOReL: Model-Based Offline Reinforcement Learning—0
On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems—0
On the Convergence of Smooth Regularized Approximate Value Iteration Schemes—0
Reinforcement Learning for Control with Multiple Frequencies—0
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations—0
Obtain Employee Turnover Rate and Optimal Reduction Strategy Based On Neural Network and Reinforcement Learning—0
Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning—0
Warren at SemEval-2020 Task 4: ALBERT and Multi-Task Learning for Commonsense Validation—0
Online Decision Based Visual Tracking via Reinforcement Learning—0
Soft-Robust Algorithms for Batch Reinforcement Learning—0
Optimizing the Neural Architecture of Reinforcement Learning AgentsCode0
UniCon: Universal Neural Controller For Physics-based Character Motion—0
IV-Posterior: Inverse Value Estimation for Interpretable Policy Certificates—0
Continuous Transition: Improving Sample Efficiency for Continuous Control Problems via MixUpCode0
Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems—0
Cluster Based Deep Contextual Reinforcement Learning for top-k Recommendations—0
Hybrid Imitation Learning for Real-Time Service Restoration in Resilient Distribution Systems—0
Optimal Mixture Weights for Off-Policy Evaluation with Multiple Behavior Policies—0
Minimax Sample Complexity for Turn-based Stochastic Game—0
Offline Reinforcement Learning Hands-On—0
Human-Agent Cooperation in Bridge Bidding—0
Adaptable Automation with Modular Deep Reinforcement Learning and Policy Transfer—0
A survey of benchmarking frameworks for reinforcement learning—0
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Benchmark Results

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