Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Treetanthiploet, Tanut"'
We study the loan contracts offered by decentralised loan protocols (DLPs) through the lens of financial derivatives. DLPs, which effectively are clearinghouses, facilitate transactions between option buyers (i.e. borrowers) and option sellers (i.e.
Externí odkaz:
http://arxiv.org/abs/2409.04233
We analyse the regret arising from learning the price sensitivity parameter $\kappa$ of liquidity takers in the ergodic version of the Avellaneda-Stoikov market making model. We show that a learning algorithm based on a regularised maximum-likelihood
Externí odkaz:
http://arxiv.org/abs/2409.02025
Combining model-based and model-free reinforcement learning approaches, this paper proposes and analyzes an $\epsilon$-policy gradient algorithm for the online pricing learning task. The algorithm extends $\epsilon$-greedy algorithm by replacing gree
Externí odkaz:
http://arxiv.org/abs/2405.03624
Autor:
Treetanthiploet, Tanut, Zhang, Yufei, Szpruch, Lukasz, Bowers-Barnard, Isaac, Ridley, Henrietta, Hickey, James, Pearce, Chris
The emergence of price comparison websites (PCWs) has presented insurers with unique challenges in formulating effective pricing strategies. Operating on PCWs requires insurers to strike a delicate balance between competitive premiums and profitabili
Externí odkaz:
http://arxiv.org/abs/2308.06935
This work uses the entropy-regularised relaxed stochastic control perspective as a principled framework for designing reinforcement learning (RL) algorithms. Herein agent interacts with the environment by generating noisy controls distributed accordi
Externí odkaz:
http://arxiv.org/abs/2208.04466
We develop a probabilistic framework for analysing model-based reinforcement learning in the episodic setting. We then apply it to study finite-time horizon stochastic control problems with linear dynamics but unknown coefficients and convex, but pos
Externí odkaz:
http://arxiv.org/abs/2112.10264
Autor:
Cohen, Samuel, Treetanthiploet, Tanut
The Asymptotic Randomised Control (ARC) algorithm provides a rigorous approximation to the optimal strategy for a wide class of Bayesian bandits, while retaining low computational complexity. In particular, the ARC approach provides nearly optimal ch
Externí odkaz:
http://arxiv.org/abs/2102.04263
We consider a general multi-armed bandit problem with correlated (and simple contextual and restless) elements, as a relaxed control problem. By introducing an entropy regularisation, we obtain a smooth asymptotic approximation to the value function.
Externí odkaz:
http://arxiv.org/abs/2010.07252
We study dynamic allocation problems for discrete time multi-armed bandits under uncertainty, based on the the theory of nonlinear expectations. We show that, under strong independence of the bandits and with some relaxation in the definition of opti
Externí odkaz:
http://arxiv.org/abs/1907.05689
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