What is multi-armed bandit problem in reinforcement learning?

Multi-Arm Bandit is a classic reinforcement learning problem, in which a player is facing with k slot machines or bandits, each with a different reward distribution, and the player is trying to maximise his cumulative reward based on trials.

What kind of problems might multi-armed bandits work on?

In practice, multi-armed bandits have been used to model problems such as managing research projects in a large organization like a science foundation or a pharmaceutical company. In early versions of the problem, the gambler begins with no initial knowledge about the machines.

What is multi-armed bandit model?

The multi-armed bandit model is a simplified version of reinforcement learning, in which there is an agent interacting with an environment by choosing from a finite set of actions and collecting a non-deterministic reward depending on the action taken.

How does multi-armed bandit work?

The term “multi-armed bandit” comes from a hypothetical experiment where a person must choose between multiple actions (i.e., slot machines, the “one-armed bandits”), each with an unknown payout. The goal is to determine the best or most profitable outcome through a series of choices.

What is bandit optimization?

Bandit optimization allocates traffic more efficiently among these discrete choices by sequentially updating the allocation of traffic based on each candidate’s performance so far. …

What is exploration and exploitation?

Exploration involves activities such as search, variation, risk taking, experimentation, discovery, and innovation. Exploitation involves activities such as refinement, efficiency, selection, implementation, and execution (March, 1991).

How do you solve a multi-armed bandit problem?

The approach to get around this could be to favour exploration of arms with a strong potential in order to get an optimal value. Upper Confidence Bound (UCB) is the most widely used solution method for multi-armed bandit problems. This algorithm is based on the principle of optimism in the face of uncertainty.

Are multi-arm bandit algorithms biologically plausible?

This suggests that the optimal solutions to multi-arm bandit problems are biologically plausible, despite being computationally demanding. UCBC (Historical Upper Confidence Bounds with clusters): The algorithm adapts UCB for a new setting such that it can incorporate both clustering and historical information.

Do multi-armed bandits win slots faster?

In theory, multi-armed bandits should produce faster results since there is no need to wait for a single winning variation. The term “multi-armed bandit” comes from a hypothetical experiment where a person must choose between multiple actions (i.e. slot machines, the “one-armed bandits”), each with an unknown payout.

What are the practical applications of the bandit model?

There are many practical applications of the bandit model, for example: clinical trials investigating the effects of different experimental treatments while minimizing patient losses, adaptive routing efforts for minimizing delays in a network, financial portfolio design