# What is the underlying logic of Adding an Environment Wrapper in ReinforcementLearning.jl?
**URL:** https://discourse.julialang.org/t/what-is-the-underlying-logic-of-adding-an-environment-wrapper-in-reinforcementlearning-jl/99539
**Category:** Machine Learning
**Tags:** question, package
**Created:** [May 29, 2023, 6:23am UTC](https://discourse.julialang.org/t/what-is-the-underlying-logic-of-adding-an-environment-wrapper-in-reinforcementlearning-jl/99539 "2023-05-29T06:23:17Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@WuSiren](https://discourse.julialang.org/u/WuSiren)
#### Post date: [May 29, 2023, 6:23am UTC](https://discourse.julialang.org/t/what-is-the-underlying-logic-of-adding-an-environment-wrapper-in-reinforcementlearning-jl/99539/1 "2023-05-29T06:23:17Z")
When customizing an environment using `ReinforcementLearning.jl`, we need to transform the environment using some environment wrappers because a `TabularQApproximator` only accepts states of type `Int`, as the [example code](https://juliareinforcementlearning.org/docs/How_to_write_a_customized_environment/#Add-an-Environment-Wrapper) shows:
```julia
wrapped_env = ActionTransformedEnv(
StateTransformedEnv(
env;
state_mapping=s -> s ? 1 : 2,
state_space_mapping = _ -> Base.OneTo(2)
);
action_mapping = i -> action_space(env)[i],
action_space_mapping = _ -> Base.OneTo(3),
)
```
Then what’s the underlying logic of this code? Specifically, why does `state_mapping=s -> s ? 1 : 2` while `action_mapping = i -> action_space(env)[i]`, which means these two mappings function in opposite directions?
---
### Author:  [@albheim](https://discourse.julialang.org/u/albheim)
#### Post date: [May 29, 2023, 7:25am UTC](https://discourse.julialang.org/t/what-is-the-underlying-logic-of-adding-an-environment-wrapper-in-reinforcementlearning-jl/99539/2 "2023-05-29T07:25:44Z")
Haven’t checked the example you are talking about, but the it seems like `env` here is an environment that represent its state as a boolean and its action as one out of three values (I guess it might be (-1, 0, 1), but haven’t check the example so not sure).
To make this into indexes for the Q-table so the agent can easily handle this, we want to convert them to be in the range 1-2 and 1-3.
So the transformations simply acts as a layer to easily define the layer that translates between env and agent representations. When the agent reads a state it is transformed from bool to 1-2, and when it supplies an action as 1-3 it is transformed to whatever the action space is for the env.