Implementation of popular bandit algorithms in batch environments.

Source code to our papers:

• "The Impact of Batch Learning in Stochastic Linear Bandits" accepted to ICDM 2022, see here for full text;
• "The Impact of Batch Learning in Stochastic Bandits" accepted at the workshop on the Ecological Theory of Reinforcement Learning, NeurIPS 2021.

The repository provides an opportunuty to run simulations or replay logged datasets in sequential batch manner - sequential interaction with the environment when responses are grouped in batches and observed by the agent only at the end of each batch. Broadly speaking, sequential batch learning is a more generalized way of learning which covers both offline and online settings as special cases bringing together their advantages.

Two particularly useful versions of the multi-armed bandit problem are implemented: Stochastic Multi-Armed Bandit (MAB) and Contextual Multi-Armed Bandit (CMAB). The key feature of the project is that both versions support parameter batch_size - a certain period of time when the agent interacts with the environment "blindly". Despite the batch setting is a property of the environment, this limitation is considered from a policy perspective. With this, it is assumed that it is not the online agent who works with the batch environment, but the batch policy interacts with the online environment.

The project is built upon RL-GLue framework, which provides an interface to connect agents, environments, and experiment programs. Note, that MAB/rl_glue.py and CMAB/rl_glue.py were adapted to make batch interaction possible.