# Basic

There are two basic concepts in reinforcement learning: the environment (namely, the outside world) and the agent (namely, the algorithm you are writing). The agent sends actions to the environment, and the environment replies with observations and rewards (that is, a score).

The core gym interface is [Env](https://github.com/openai/gym/blob/master/gym/core.py), which is the unified environment interface. There is no interface for agents; that part is left to you. The following are the `Env` methods you should know:

* reset(self): Reset the environment's state. Returns observation.
* step(self, action): Step the environment by one timestep. Returns observation, reward, done, info.
* render(self, mode='human'): Render one frame of the environment. The default mode will do something human friendly, such as pop up a window.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://alfredo-reyes-montero.gitbook.io/openai/products/gym/basic.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.