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Found 8 papers, 0 papers with code
ZeroSwap: Data-driven Optimal Market Making in DeFi
This ensures that the market maker balances losses to informed traders with profits from noise traders.
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret
While quantum reinforcement learning (RL) has attracted a surge of attention recently, its theoretical understanding is limited.
Provable Sim-to-real Transfer in Continuous Domain with Partial Observations
Sim-to-real transfer trains RL agents in the simulated environments and then deploys them in the real world.
Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver
In particular, we take a plug-in solver approach, where we focus on learning a model in the exploration phase and demand that \emph{any planning algorithm} on the learned model can give a near-optimal policy.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
Understanding Domain Randomization for Sim-to-real Transfer
Reinforcement learning encounters many challenges when applied directly in the real world.
Near-optimal Representation Learning for Linear Bandits and Linear RL
This paper studies representation learning for multi-task linear bandits and multi-task episodic RL with linear value function approximation.
Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL
The regret of FMDP-BF is shown to be exponentially smaller than that of optimal algorithms designed for non-factored MDPs, and improves on the best previous result for FMDPs~\citep{osband2014near} by a factored of $\sqrt{H|\mathcal{S}_i|}$, where $|\mathcal{S}_i|$ is the cardinality of the factored state subspace and $H$ is the planning horizon.
Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication
We study the problem of regret minimization for distributed bandits learning, in which $M$ agents work collaboratively to minimize their total regret under the coordination of a central server.
