semvec/pypi-packages huggingface dataset:
trevis 0.0.1
trevis
Requires Python: >=3.6

# Trevis

Easily visualize recursive functions in the console.

```python
from trevis import recursion

@recursion
def fib(n: int) -> int:
    if n < 2: return n
    return fib(n - 1) + fib(n - 2)

fib(4)
```
```python
# output
fib(4) → 3
├╴fib(3) → 2
│ ├╴fib(2) → 1
│ │ ├╴fib(1) → 1
│ │ └╴fib(0) → 0
│ └╴fib(1) → 1
└╴fib(2) → 1
  ├╴fib(1) → 1
  └╴fib(0) → 0
```
## Features

- 🕹️ **Interactive mode**: Review each function execution interactively, advancing to the next step by pressing Enter. This mode enhances comprehension and debugging by providing a clear visual representation of each step. Enable with `recursion(interactive=True)`.
- 🌈 **Syntax highlighting (*upcoming*)**: Enable syntax highlighting in the tree for improved readability. Coming in a future version.
- 🎲 **Non-deterministic function visualization (*upcoming*)**: Visualize of all possible execution paths for non-deterministic functions. Coming in a future version.

## Installation

Ensure you have Python 3.6 or higher, then install Trevis using pip:

```bash
pip install trevis
```

## Contribution

Contributions are welcome! Feel free to submit an [issue](https://github.com/cabralpinto/trevis/issues) or [pull request](https://github.com/cabralpinto/trevis/pulls).

## License

Trevis is licensed under the [MIT License](LICENSE).
embodied 1.2.0
Fast reinforcement learning research

[![PyPI](https://img.shields.io/pypi/v/embodied.svg)](https://pypi.python.org/pypi/embodied/#history) &nbsp;
[![Docs](https://readthedocs.org/projects/embodied/badge)](https://embodied.readthedocs.org)

# Embodied

Fast reinforcement learning research.

## Overview

The goal of Embodied is to empower researchers to quickly implement new agents
at scale. Embodied achieves this by specifying an interface both for
environments and agents, allowing users to mix and match agents, envs, and
evaluation protocols. Embodied provides common building blocks that users are
encouraged to fork when more control is needed. The only dependency is Numpy
and agents can be implemented in any framework.

## Packages

```
embodied/
  core/    # Config, logging, checkpointing, simulation, wrappers
  run/     # Evaluation protocols that combine agents and environments
  envs/    # Environment suites such as Gym, Atari, DMC, Crafter
  agents/  # Agent implementations
```

## Agent API

```python
class Agent:
  __init__(obs_space, act_space, config)
  policy(obs, carry, mode='train') -> act, carry
  train(data, carry) -> metrics, carry
  report(data, carry) -> metrics, carry
  init_policy(batch_size) -> carry
  init_train(batch_size) -> carry
  init_report(batch_size) -> carry
  dataset(generator) -> generator
```

## Env API

```python
class Env:
  __len__() -> int
  @obs_space -> dict of spaces
  @act_space -> dict of spaces
  step(act) -> obs dict
  render() -> array
  close()
```