Contents

- 1 How does learning to optimize with reinforcement learning work?
- 2 How is reinforcement learning used in task scheduling?
- 3 How to solve the scheduling problem in distributed systems?
- 4 How is reinforcement learning used in distributed systems?
- 5 How are neural nets used in reinforcement learning?
- 6 Is the learning rule dependent on the objective function?
- 7 How is the capacity of a base model constrained?
- 8 How are step vectors used in reinforcement learning?
- 9 Why are machine learning algorithms still designed manually?
- 10 Which is the best deep reinforcement learning approach?
- 11 How is power minimization used in deep reinforcement learning?
- 12 Why do we need to learn optimization algorithms?
- 13 Which is a branch of ML called reinforcement learning?
- 14 When to use optimizer learning for learning to learn?

## How does learning to optimize with reinforcement learning work?

In our paper last year ( Li & Malik, 2016 ), we introduced a framework for learning optimization algorithms, known as “Learning to Optimize”. We note that soon after our paper appeared, ( Andrychowicz et al., 2016) also independently proposed a similar idea. Consider how existing continuous optimization algorithms generally work.

## How is reinforcement learning used in task scheduling?

Q -learning and state–action–reward–state–action methods. DAG scheduling on dynamic clusters. Variable tasks and task classification.

## How to solve the scheduling problem in distributed systems?

This paper proposes a reinforcement learning algorithm to solve the scheduling problem in distributed systems.

## How is reinforcement learning used in distributed systems?

With the rise of social media applications and smart devices, the amount of digital data and the velocity at which it is produced have increased exponentially, determining the development of distributed system frameworks and platforms that increase productivity, consistency, fault-tolerance and security of parallel applications.

## How are neural nets used in reinforcement learning?

Parameterizing the update formula as a neural net has two appealing properties mentioned earlier: first, it is expressive, as neural nets are universal function approximators and can in principle model any update formula with sufficient capacity; second, it allows for efficient search, as neural nets can be trained easily with backpropagation.

## Is the learning rule dependent on the objective function?

The learning rule depends on a subset of the dimensions of the current iterate encoding the activities of neighbouring neurons, but does not depend on the objective function and therefore does not have the capability to generalize to different objective functions.

## How is the capacity of a base model constrained?

Because the base-model is encoded in the recurrent net’s memory state, its capacity is constrained by the memory size. A related area is hyperparameter optimization, which aims for a weaker goal and searches over base-models parameterized by a predefined set of hyperparameters.

## How are step vectors used in reinforcement learning?

They operate in an iterative fashion and maintain some iterate, which is a point in the domain of the objective function. Initially, the iterate is some random point in the domain; in each iteration, a step vector is computed using some fixed update formula, which is then used to modify the iterate.

## Why are machine learning algorithms still designed manually?

This success can be attributed to the data-driven philosophy that underpins machine learning, which favours automatic discovery of patterns from data over manual design of systems using expert knowledge. Yet, there is a paradox in the current paradigm: the algorithms that power machine learning are still designed manually.

## Which is the best deep reinforcement learning approach?

We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences.

## How is power minimization used in deep reinforcement learning?

In this paper, we minimize the AP’s transmit power by a joint optimization of the AP’s active beamforming and the IRS’s passive beamforming. Due to uncertain channel conditions, we formulate a robust power minimization problem subject to the receiver’s signal-to-noise ratio (SNR) requirement and the IRS’s power budget constraint.

## Why do we need to learn optimization algorithms?

There are two reasons: first, many optimization algorithms are devised under the assumption of convexity and applied to non-convex objective functions; by learning the optimization algorithm under the same setting as it will actually be used in practice, the learned optimization algorithm could hopefully achieve better performance.

## Which is a branch of ML called reinforcement learning?

A particular branch of ML that we consider in this survey is called reinforcement learning (RL) that for a given CO problem defines an environment and the agent that acts in the environment constructing a solution.

## When to use optimizer learning for learning to learn?

Under this setting, optimizer learning can be used for “learning to learn”. For clarity, we will refer to the model that is trained using the optimizer as the “base-model” and prefix common terms with “base-” and “meta-” to disambiguate concepts associated with the base-model and the optimizer respectively.