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Found 7 papers, 1 papers with code
Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition
We consider the task of learning in episodic finite-horizon Markov decision processes with an unknown transition function, bandit feedback, and adversarial losses.
Heterogeneous Directed Hypergraph Neural Network over abstract syntax tree (AST) for Code Classification
In this study, we propose to represent AST as a heterogeneous directed hypergraph (HDHG) and process the graph by heterogeneous directed hypergraph neural network (HDHGN) for code classification.
Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback
The standard assumption in reinforcement learning (RL) is that agents observe feedback for their actions immediately.
The best of both worlds: stochastic and adversarial episodic MDPs with unknown transition
We consider the best-of-both-worlds problem for learning an episodic Markov Decision Process through $T$ episodes, with the goal of achieving $\widetilde{\mathcal{O}}(\sqrt{T})$ regret when the losses are adversarial and simultaneously $\mathcal{O}(\text{polylog}(T))$ regret when the losses are (almost) stochastic.
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition
This work studies the problem of learning episodic Markov Decision Processes with known transition and bandit feedback.
Learning Adversarial MDPs with Bandit Feedback and Unknown Transition
We consider the problem of learning in episodic finite-horizon Markov decision processes with an unknown transition function, bandit feedback, and adversarial losses.
Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem
Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ride-sharing platform marketplace.
