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@ -10,192 +10,64 @@ Built upon the foundation of [Gymnasium](https://gymnasium.farama.org/) (a maint
**Key Features**:
- **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.
- **New Challenging Environments**: `fancy_gym` includes several new environments ([Panda Box Pushing](https://dominik-roth.eu/fancy/envs/fancy/mujoco.html#box-pushing), [Table Tennis](https://dominik-roth.eu/fancy/envs/fancy/mujoco.html#table-tennis), [etc.](https://dominik-roth.eu/fancy/envs/fancy/index.html)) 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.
- **Contribute Your Own Environments**: If you're inspired to create custom gym environments, both step-based and with movement primitives, this [guide](https://gymnasium.farama.org/tutorials/gymnasium_basics/environment_creation/) will assist you. We encourage and highly appreciate submissions via PRs to integrate these environments into `fancy_gym`.
- **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](dominik-roth.eu/fancy/envs/dmc.html)
and [Metaworld](https://dominik-roth.eu/fancy/envs/meta.html), whether you want to use them in the regular step-based setting or using MPs.
- **Contribute Your Own Environments**: If you’re inspired to create custom gym environments, both step-based and with movement primitives, this [guide](https://dominik-roth.eu/fancy/guide/upgrading_envs.html) will assist you. We encourage and highly appreciate submissions via PRs to integrate these environments into `fancy_gym`.
## Movement Primitive Environments (Episode-Based/Black-Box Environments)
## Quickstart Guide
<p align="justify">
In step-based environments, actions are determined step by step, with each individual observation directly mapped to a corresponding action. Contrary to this, in episodic MP-based (Movement Primitive-based) environments, the process is different. Here, rather than responding to individual observations, a broader context is considered at the start of each episode. This context is used to define parameters for Movement Primitives (MPs), which then describe a complete trajectory. The trajectory is executed over the entire episode using a tracking controller, allowing for the enactment of complex, continuous sequences of actions. This approach contrasts with the discrete, moment-to-moment decision-making of step-based environments and integrates concepts from stochastic search and black-box optimization, commonly found in classical robotics and control.
</p>
| &#x2139; We recommend installing `fancy_gym` into a virtual environment as provided by [venv](https://docs.python.org/3/library/venv.html), [Poetry](https://python-poetry.org/) or [Conda](https://docs.conda.io/en/latest/).
|
| ----------------------------------------------------------------------------------------------------------------------------------------------- |
For a more extensive explaination, please have a look at our Documentation-TODO:Link.
## Installation
We recommend installing `fancy_gym` into a virtual environment as provided by [venv](https://docs.python.org/3/library/venv.html). 3rd party alternatives to venv like [Poetry](https://python-poetry.org/) or [Conda](https://docs.conda.io/en/latest/) can also be used.
### Installation from PyPI (recommended)
Install `fancy_gym` via
Install via pip (`or use an alternative installation method <guide/installation.html>`\_\_)
```bash
pip install fancy_gym
pip install 'fancy_gym[all]'
```
We have a few optional dependencies. If you also want to install those use
```bash
# to install all optional dependencies
pip install 'fancy_gym[all]'
# or choose only those you want
pip install 'fancy_gym[dmc,box2d,mujoco-legacy,jax,testing]'
```
Pip can not automatically install up-to-date versions of metaworld, since they are not avaible on PyPI yet.
Install metaworld via
```bash
pip install metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@d155d0051630bb365ea6a824e02c66c068947439#egg=metaworld
```
### Installation from master
1. Clone the repository
```bash
git clone git@github.com:ALRhub/fancy_gym.git
```
2. Go to the folder
```bash
cd fancy_gym
```
3. Install with
```bash
pip install -e .
```
We have a few optional dependencies. If you also want to install those use
```bash
# to install all optional dependencies
pip install -e '.[all]'
# or choose only those you want
pip install -e '.[dmc,box2d,mujoco-legacy,jax,testing]'
```
Metaworld has to be installed manually with
```bash
pip install metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@d155d0051630bb365ea6a824e02c66c068947439#egg=metaworld
```
## How to use Fancy Gym
Documentation for `fancy_gym` is avaible at TODO:Link. Usage examples can be found here-TODO:Link.
### Step-Based Environments
Regular step based environments added by Fancy Gym are added into the `fancy/` namespace.
| &#x2757; Legacy versions of Fancy Gym used `fancy_gym.make(...)`. This is no longer supported and will raise an Exception on new versions. |
| ------------------------------------------------------------------------------------------------------------------------------------------ |
Try out one of our step-based environments (`or explore our other envs <envs/fancy/index.html>`\_\_)
```python
import gymnasium as gym
import fancy_gym
import gymnasium as gym
import fancy_gym
import time
env = gym.make('fancy/Reacher5d-v0')
# or env = gym.make('metaworld/reach-v2') # fancy_gym allows access to all metaworld ML1 tasks via the metaworld/ NS
# or env = gym.make('dm_control/ball_in_cup-catch-v0')
# or env = gym.make('Reacher-v2')
observation = env.reset(seed=1)
env = gym.make('fancy/BoxPushingDense-v0', render_mode='human')
observation = env.reset()
env.render()
for i in range(1000):
action = env.action_space.sample()
observation, reward, terminated, truncated, info = env.step(action)
if i % 5 == 0:
env.render()
for i in range(1000):
action = env.action_space.sample() # Randomly sample an action
observation, reward, terminated, truncated, info = env.step(action)
time.sleep(1/env.metadata['render_fps'])
if terminated or truncated:
observation, info = env.reset()
if terminated or truncated:
observation, info = env.reset()
```
A list of all included environments is avaible here-TODO:Link.
### Black-box Environments
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.
Explore the MP-based variant (`or learn more about Movement Primitives (MPs) <guide/episodic_rl.html>`\_\_)
```python
import gymnasium as gym
import fancy_gym
import gymnasium as gym
import fancy_gym
env = gym.make('fancy_ProMP/Reacher5d-v0')
# or env = gym.make('metaworld_ProDMP/reach-v2')
# or env = gym.make('dm_control_DMP/ball_in_cup-catch-v0')
# or env = gym.make('gym_ProMP/Reacher-v2') # mp versions of envs added directly by gymnasium are in the gym_<MP-type> NS
env = gym.make('fancy_ProMP/BoxPushingDense-v0', render_mode='human')
env.reset()
env.render()
# render() can be called once in the beginning with all necessary arguments.
# To turn it of again just call render() without any arguments.
env.render(mode='human')
# This returns the context information, not the full state observation
observation, info = env.reset(seed=1)
for i in range(5):
action = env.action_space.sample()
observation, reward, terminated, truncated, info = env.step(action)
# terminated or truncated is always True as we are working on the episode level, hence we always reset()
observation, info = env.reset()
for i in range(10):
action = env.action_space.sample() # Randomly sample MP parameters
observation, reward, terminated, truncated, info = env.step(action) # Will execute full trajectory, based on MP
observation = env.reset()
```
A list of all included MP environments is avaible here-TODO:Link.
## Documentation
### How to create a new MP task
We refer to our Documentation for a complete description-TODO:Link.
If the step-based is already registered with gym, you can simply do the following:
```python
fancy_gym.upgrade(
id='custom/cool_new_env-v0',
mp_wrapper=my_custom_MPWrapper
)
```
If the step-based is not yet registered with gym we can add both the step-based and MP-versions via
```python
fancy_gym.register(
id='custom/cool_new_env-v0',
entry_point=my_custom_env,
mp_wrapper=my_custom_MPWrapper
)
```
As for how to write custom MP-Wrappers, please have a look at our Documentation-TODO:Link.
From this point on, you can access MP-version of your environments via
```python
env = gym.make('custom_ProDMP/cool_new_env-v0')
rewards = 0
observation, info = env.reset()
# number of samples/full trajectories (multiple environment steps)
for i in range(5):
ac = env.action_space.sample()
observation, reward, terminated, truncated, info = env.step(ac)
rewards += reward
if terminated or truncated:
print(rewards)
rewards = 0
observation, info = env.reset()
```
Documentation for `fancy_gym` can be found [here](https://dominik-roth.eu/fancy); Usage Examples can be found [here](https://dominik-roth.eu/fancy/examples/general.html).
## Citing the Project