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Found 17 papers, 0 papers with code
Learning Merton's Strategies in an Incomplete Market: Recursive Entropy Regularization and Biased Gaussian Exploration
We study Merton's expected utility maximization problem in an incomplete market, characterized by a factor process in addition to the stock price process, where all the model primitives are unknown.
Robust utility maximization with intractable claims
We study a continuous-time expected utility maximization problem in which the investor at maturity receives the value of a contingent claim in addition to the investment payoff from the financial market.
Variable Clustering via Distributionally Robust Nodewise Regression
We study a multi-factor block model for variable clustering and connect it to the regularized subspace clustering by formulating a distributionally robust version of the nodewise regression.
Naive Markowitz Policies
We study a continuous-time Markowitz mean-variance portfolio selection model in which a naive agent, unaware of the underlying time-inconsistency, continuously reoptimizes over time.
Square-root regret bounds for continuous-time episodic Markov decision processes
We study reinforcement learning for continuous-time Markov decision processes (MDPs) in the finite-horizon episodic setting.
Choquet regularization for reinforcement learning
We propose \emph{Choquet regularizers} to measure and manage the level of exploration for reinforcement learning (RL), and reformulate the continuous-time entropy-regularized RL problem of Wang et al. (2020, JMLR, 21(198)) in which we replace the differential entropy used for regularization with a Choquet regularizer.
q-Learning in Continuous Time
We study the continuous-time counterpart of Q-learning for reinforcement learning (RL) under the entropy-regularized, exploratory diffusion process formulation introduced by Wang et al. (2020).
Logarithmic regret bounds for continuous-time average-reward Markov decision processes
We consider reinforcement learning for continuous-time Markov decision processes (MDPs) in the infinite-horizon, average-reward setting.
Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithms
This effectively turns PG into a policy evaluation (PE) problem, enabling us to apply the martingale approach recently developed by Jia and Zhou (2021) for PE to solve our PG problem.
Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach
From this perspective, we find that the mean--square TD error approximates the quadratic variation of the martingale and thus is not a suitable objective for PE.
Asset Selection via Correlation Blockmodel Clustering
Finally, we conduct an empirical analysis to verify the performance of the algorithm.
When to Quit Gambling, if You Must!
We develop an approach to solve Barberis (2012)'s casino gambling model in which a gambler whose preferences are specified by the cumulative prospect theory (CPT) must decide when to stop gambling by a prescribed deadline.
Simulated annealing from continuum to discretization: a convergence analysis via the Eyring--Kramers law
in cumulative step size), and provide an explicit rate as a function of the model parameters.
State-Dependent Temperature Control for Langevin Diffusions
We study the temperature control problem for Langevin diffusions in the context of non-convex optimization.
Variance Contracts
We study the design of an optimal insurance contract in which the insured maximizes her expected utility and the insurer limits the variance of his risk exposure while maintaining the principle of indemnity and charging the premium according to the expected value principle.
Consistent Investment of Sophisticated Rank-Dependent Utility Agents in Continuous Time
We study portfolio selection in a complete continuous-time market where the preference is dictated by the rank-dependent utility.
Continuous-Time Mean-Variance Portfolio Selection: A Reinforcement Learning Framework
We approach the continuous-time mean-variance (MV) portfolio selection with reinforcement learning (RL).
