Contents

- 1 What is a horizon in reinforcement learning?
- 2 What is a finite horizon?
- 3 What is lifelong reinforcement learning?
- 4 What is an infinite horizon MDP?
- 5 What is infinite horizon dynamic optimization problem?
- 6 What are the important component of reinforcement learning?
- 7 What is a time horizon in marketing?
- 8 What is the difference between an indefinite horizon and an infinite horizon?
- 9 How are model free algorithms used in infinite horizon?
- 10 Which is the best model free reinforcement learning algorithm?
- 11 How are value functions trained for long horizon?
- 12 How are \\ gamma models used in reinforcement learning?

## What is a horizon in reinforcement learning?

Horizon is an end-to-end platform designed to solve industry applied RL problems where datasets are large (millions to billions of observations), the feedback loop is slow (vs. Deep reinforcement learning (RL) is poised to revolution- ize how autonomous systems are built.

## What is a finite horizon?

So when someone says “finite horizon” what they mean is describing the value the agent can achieve over only a finite number of steps into the future from its current state, rather than an infinite horizon, in which the agent cares about reward over all possible future steps.

## What is lifelong reinforcement learning?

Lifelong reinforcement learning provides a promising framework for developing versatile agents that can accumulate knowledge over a lifetime of experience and rapidly learn new tasks by building upon prior knowledge.

## What is an infinite horizon MDP?

Infinite horizon MDP’s do not care about the initial state. They attempt to be optimal in the sense that the policy is optimal for all given allowable initial states. Finite MDP’s are computed as optimal for a given state. Time optimal control cannot be performed via the infinite horizon case or is not recommended.

## What is infinite horizon dynamic optimization problem?

Multiprocess problems are dynamic optimization problems in which there is a collection of control systems coupled through constraints in the endpoints of the constituent trajectories and through the cost function. Optimality conditions for such problems posed over a finite interval have already been derived.

## What are the important component of reinforcement learning?

Beyond the agent and the environment, one can identify four main subelements of a reinforcement learning system: a policy, a reward function, a value function, and, optionally, a model of the environment. A policy defines the learning agent’s way of behaving at a given time.

## What is a time horizon in marketing?

A time horizon or investment horizon is the total length of time a security is expected to be held by an investor. Normally, with a long-term horizon, investors feel more comfortable to take riskier investment decisions and capitalize on the market volatility.

## What is the difference between an indefinite horizon and an infinite horizon?

Often an agent must reason about an ongoing process or it does not know how many actions it will be required to do. These are called infinite horizon problems when the process may go on forever or indefinite horizon problems when the agent will eventually stop, but where it does not know when it will stop.

## How are model free algorithms used in infinite horizon?

In this paper, two model-free algorithms are introduced for learning infinite-horizon average-reward Markov Decision Processes (MDPs). The first algorithm reduces the problem to the discounted-reward version and achieves $\\mathcal{O}(T^{2/3})$ regret after $T$ steps, under the minimal assumption of weakly communicating MDPs.

## Which is the best model free reinforcement learning algorithm?

Model-free reinforcement learning is known to be memory and computation efficient and more amendable to large scale problems. In this paper, two model-free algorithms are introduced for learning… Donate to arXiv

## How are value functions trained for long horizon?

In order to amortize this long-horizon prediction, value functions are trained with either Monte Carlo estimates of expected cumulative reward or with dynamic programming. The important distinction is now that the long-horizon nature of the prediction task is dealt with during training instead of during testing.

## How are \\ gamma models used in reinforcement learning?

We can derive an analogous strategy with a \\gamma -model, called \\gamma -MVE, that features a gradual transition between model-based and model-free value estimation. This value estimation strategy can be incorporated into a model-based reinforcement learning algorithm for improved sample-efficiency.