# discount¶

Note

Discount factors are only implemented in the learning mechanisms *Guided associative learning*, *Expected SARSA*, *Q-learning*, and *Actor-critic*.

Specifies the discount factor, written as \(\gamma\) in the equations for memory updates, that tells how important future rewards are to the current state. The discount factor is a value between 0 and 1. A reinforcement value \(u\) that occurs \(N\) steps in the future from the current state, is multiplied by \(\gamma^N\) to describe its importance to the current state. For example, consider \(\gamma=0.9\) and a reinforcement value \(u=10\) that is 3 steps ahead of the current state. The importance of this reward to the subject from where it stands is equal to \(10 \cdot 0.9^3 = 7.29\).

The value of the parameter `discount`

is used in the updating equations described in the mechanisms.

## Syntax¶

```
discount = v
```

where `v`

is a scalar expression.

## Description¶

`discount = v`

sets the discount factor to `v`

.

## Examples¶

```
discount = 0.5
```

sets the discount factor to 0.5.

```
@variables x = 0.5
discount = x + 0.1
```

sets the discount factor to 0.6.