LeanMachineLearning

3.4. SequentialLearning.StationaryEnv🔗

Oblivious and stationary environments

An oblivious environment is an environment in which the distribution of the next feedback depends only on the last action (and not on the past history). If the kernel that gives the distribution of the next feedback given the last action is the same at every time step, then we say that the environment is stationary.

Main definitions

We define a Prop-valued typeclass IsObliviousEnv to express that an environment is oblivious, and we define two constructors for oblivious environments.

Typeclass and related definitions:

  • IsObliviousEnv env: the environment env is oblivious.

  • feedbackCondAction env n: the kernel representing the conditional distribution of the feedback given the action at time n in an oblivious environment env.

Constructors for oblivious environments:

  • obliviousEnv ν: an oblivious environment, in which the distribution of the next feedback depends only on the last action, but in a possibly time-dependent manner, and is given by a sequence of Markov kernels ν : ℕ → Kernel 𝓐 𝓨.

  • stationaryEnv ν: a stationary environment, in which the distribution of the next feedback depends only on the last action (and not on the past history), and is given by a Markov kernel ν : Kernel 𝓐 𝓨.

Module LeanMachineLearning.SequentialLearning.StationaryEnv contains 28 exposed declarations.