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Found 4 papers, 1 papers with code
An Information-Theoretic Analysis of Nonstationary Bandit Learning
In nonstationary bandit learning problems, the decision-maker must continually gather information and adapt their action selection as the latent state of the environment evolves.
Risk-Sensitive Optimal Execution via a Conditional Value-at-Risk Objective
As our problem is a special case of a linear-quadratic-Gaussian control problem with a CVaR objective, these results may be interesting in broader settings.
Policy Gradient Optimization of Thompson Sampling Policies
We study the use of policy gradient algorithms to optimize over a class of generalized Thompson sampling policies.
Thompson Sampling with Information Relaxation Penalties
With this framework, we define an intuitive family of control policies that include Thompson sampling (TS) and the Bayesian optimal policy as endpoints.
