dodox/fancy_gym
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

minor changes to README

Browse Source
This commit is contained in:
Dominik Moritz Roth 2023-09-20 12:49:31 +02:00
parent 6d0e96c6f2
commit d6ecc0dc67
1 changed files with 23 additions and 22 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

27
README.md
View File

@ -8,13 +8,13 @@
</h1>
| :exclamation: Fancy Gym has recently received a major refactor, which also updated many of the used dependencies to current versions. The update has brought some breaking changes. If you want to access the old version, check out the legacy branch. Find out more about what changed [here](https://github.com/ALRhub/fancy_gym/pull/75). |
| ------------------------------------------------------------ |
| --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
Built upon the foundation of [Gymnasium](https://gymnasium.farama.org/) (a maintained fork of OpenAI’s renowned Gym library) `fancy_gym` offers a comprehensive collection of reinforcement learning environments.
**Key Features**:
- **New Challenging Environments**: We've introduced several new environments (Panda Box Pushing, Table Tennis, etc.) that present a higher degree of difficulty, pushing the boundaries of reinforcement learning research.
- **New Challenging Environments**: `fancy_gym` includes several new environments (Panda Box Pushing, Table Tennis, etc.) that present a higher degree of difficulty, pushing the boundaries of reinforcement learning research.
- **Support for Movement Primitives**: `fancy_gym` supports a range of movement primitives (MPs), including Dynamic Movement Primitives (DMPs), Probabilistic Movement Primitives (ProMP), and Probabilistic Dynamic Movement Primitives (ProDMP).
- **Upgrade to Movement Primitives**: With our framework, it's straightforward to transform standard Gymnasium environments into environments that support movement primitives.
- **Benchmark Suite Compatibility**: `fancy_gym` makes it easy to access renowned benchmark suites such as [DeepMind Control](https://deepmind.com/research/publications/2020/dm-control-Software-and-Tasks-for-Continuous-Control) and [Metaworld](https://meta-world.github.io/), whether you want to use them in the regular step-based setting or using MPs.
@ -55,16 +55,16 @@ pip install -e '.[all]' # to install all optional dependencies
pip install -e '.[dmc,metaworld,box2d,mujoco,mujoco-legacy,jax,testing]' # or choose only those you want
```
## How to use Fancy Gym
We will only show the basics here and prepared [multiple examples](fancy_gym/examples/) for a more detailed look.
### Step-Based Environments
Regular step based environments added by Fancy Gym are added into the `fancy/` namespace.
| :exclamation: Legacy versions of Fancy Gym used `fancy_gym.make(...)`. This is no longer supported and will raise an Exception on new versions. |
| ------------------------------------------------------------ |
| ----------------------------------------------------------------------------------------------------------------------------------------------- |
```python
import gymnasium as gym
@ -90,15 +90,15 @@ for i in range(1000):
All environments provide by default the cumulative episode reward, this can however be changed if necessary. Optionally, each environment returns all collected information from each step as part of the infos. This information is, however, mainly meant for debugging as well as logging and not for training.
|Key| Description|Type
|---|---|---|
`positions`| Generated trajectory from MP | Optional
`velocities`| Generated trajectory from MP | Optional
`step_actions`| Step-wise executed action based on controller output | Optional
`step_observations`| Step-wise intermediate observations | Optional
`step_rewards`| Step-wise rewards | Optional
`trajectory_length`| Total number of environment interactions | Always
`other`| All other information from the underlying environment are returned as a list with length `trajectory_length` maintaining the original key. In case some information are not provided every time step, the missing values are filled with `None`. | Always
| Key | Description | Type |
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | -------- |
| `positions` | Generated trajectory from MP | Optional |
| `velocities` | Generated trajectory from MP | Optional |
| `step_actions` | Step-wise executed action based on controller output | Optional |
| `step_observations` | Step-wise intermediate observations | Optional |
| `step_rewards` | Step-wise rewards | Optional |
| `trajectory_length` | Total number of environment interactions | Always |
| `other` | All other information from the underlying environment are returned as a list with length `trajectory_length` maintaining the original key. In case some information are not provided every time step, the missing values are filled with `None`. | Always |
Existing MP tasks can be created the same way as above. The namespace of a MP-variant of an environment is given by `<original namespace>_<MP name>/`.
Just keep in mind, calling `step()` executes a full trajectory.
@ -263,4 +263,5 @@ for i in range(5):
```
## Icon Attribution
The icon is based on the [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) icon as can be found [here](https://gymnasium.farama.org/_static/img/gymnasium_black.svg).