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@ -6,9 +6,6 @@
<br><br>
</h1>
| &#x2757; 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](https://github.com/ALRhub/fancy_gym/tree/legacy). 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**:
@ -22,15 +19,11 @@ Built upon the foundation of [Gymnasium](https://gymnasium.farama.org/) (a maint
## Movement Primitive Environments (Episode-Based/Black-Box Environments)
<p align="justify">
Movement primitive (MP) environments differ from traditional step-based environments. They align more with concepts from stochastic search, black-box optimization, and methods commonly found in classical robotics and control. Instead of individual steps, MP environments operate on an episode basis, executing complete trajectories. These trajectories are produced by trajectory generators like Dynamic Movement Primitives (DMP), Probabilistic Movement Primitives (ProMP) or Probabilistic Dynamic Movement Primitives (ProDMP).
</p>
<p align="justify">
Once generated, these trajectories are converted into step-by-step actions using a trajectory tracking controller. The specific controller chosen depends on the environment's requirements. Currently, we support position, velocity, and PD-Controllers tailored for position, velocity, and torque control. Additionally, we have a specialized controller designed for the MetaWorld control suite.
</p>
<p align="justify">
While the overarching objective of MP environments remains the learning of an optimal policy, the actions here represent the parametrization of motion primitives to craft the right trajectory. Our framework further enhances this by accommodating a contextual setting. At the episode's onset, we present the context space—a subset of the observation space. This demands the prediction of a new action or MP parametrization for every unique context.
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>
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.
@ -38,6 +31,7 @@ We recommend installing `fancy_gym` into a virtual environment as provided by [v
### Installation from PyPI (recommended)
Install `fancy_gym` via
```bash
pip install fancy_gym
```
@ -62,21 +56,25 @@ pip install metaworld@git+https://github.com/Farama-Foundation/Metaworld.git@d15
### 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]'
@ -86,20 +84,21 @@ 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
We will only show the basics here and prepared [multiple examples](https://github.com/ALRhub/fancy_gym/tree/master/fancy_gym/examples/) for a more detailed look.
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. |
| ----------------------------------------------------------------------------------------------------------------------------------------------- |
| &#x2757; 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
@ -121,30 +120,13 @@ for i in range(1000):
observation, info = env.reset()
```
A list of all included environments is avaible here-TODO:Link.
### Black-box Environments
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 |
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.
> **Note:**
> Currently, we are also in the process of enabling replanning as well as learning of sub-trajectories.
> This allows to split the episode into multiple trajectories and is a hybrid setting between step-based and
> black-box leaning.
> While this is already implemented, it is still in beta and requires further testing.
> Feel free to try it and open an issue with any problems that occur.
```python
import gymnasium as gym
import fancy_gym
@ -169,121 +151,11 @@ for i in range(5):
observation, info = env.reset()
```
To show all available environments, we provide some additional convenience variables. All of them return a dictionary
with the keys `DMP`, `ProMP`, `ProDMP` and `all` that store a list of available environment ids.
```python
import fancy_gym
print("All Black-box tasks:")
print(fancy_gym.ALL_MOVEMENT_PRIMITIVE_ENVIRONMENTS)
print("Fancy Black-box tasks:")
print(fancy_gym.ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS)
print("OpenAI Gym Black-box tasks:")
print(fancy_gym.ALL_GYM_MOVEMENT_PRIMITIVE_ENVIRONMENTS)
print("Deepmind Control Black-box tasks:")
print(fancy_gym.ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS)
print("MetaWorld Black-box tasks:")
print(fancy_gym.ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS)
print("If you add custom envs, their mp versions will be found in:")
print(fancy_gym.MOVEMENT_PRIMITIVE_ENVIRONMENTS_FOR_NS['<my_custom_namespace>'])
```
A list of all included MP environments is avaible here-TODO:Link.
### How to create a new MP task
In case a required task is not supported yet in the MP framework, it can be created relatively easy. For the task at
hand, the following [interface](https://github.com/ALRhub/fancy_gym/tree/master/fancy_gym/black_box/raw_interface_wrapper.py) needs to be implemented.
```python
from abc import abstractmethod
from typing import Union, Tuple
import gymnasium as gym
import numpy as np
class RawInterfaceWrapper(gym.Wrapper):
mp_config = {
'ProMP': {},
'DMP': {},
'ProDMP': {},
}
@property
def context_mask(self) -> np.ndarray:
"""
Returns boolean mask of the same shape as the observation space.
It determines whether the observation is returned for the contextual case or not.
This effectively allows to filter unwanted or unnecessary observations from the full step-based case.
E.g. Velocities starting at 0 are only changing after the first action. Given we only receive the
context/part of the first observation, the velocities are not necessary in the observation for the task.
Returns:
bool array representing the indices of the observations
"""
return np.ones(self.env.observation_space.shape[0], dtype=bool)
@property
@abstractmethod
def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
"""
Returns the current position of the action/control dimension.
The dimensionality has to match the action/control dimension.
This is not required when exclusively using velocity control,
it should, however, be implemented regardless.
E.g. The joint positions that are directly or indirectly controlled by the action.
"""
raise NotImplementedError()
@property
@abstractmethod
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
"""
Returns the current velocity of the action/control dimension.
The dimensionality has to match the action/control dimension.
This is not required when exclusively using position control,
it should, however, be implemented regardless.
E.g. The joint velocities that are directly or indirectly controlled by the action.
"""
raise NotImplementedError()
```
Default configurations for MPs can be overitten by defining attributes in mp_config.
Available parameters are documented in the [MP_PyTorch Userguide](https://github.com/ALRhub/MP_PyTorch/blob/main/doc/README.md).
```python
class RawInterfaceWrapper(gym.Wrapper):
mp_config = {
'ProMP': {
'phase_generator_kwargs': {
'phase_generator_type': 'linear'
# When selecting another generator type, the default configuration will not be merged for the attribute.
},
'controller_kwargs': {
'p_gains': 0.5 * np.array([1.0, 4.0, 2.0, 4.0, 1.0, 4.0, 1.0]),
'd_gains': 0.5 * np.array([0.1, 0.4, 0.2, 0.4, 0.1, 0.4, 0.1]),
},
'basis_generator_kwargs': {
'num_basis': 3,
'num_basis_zero_start': 1,
'num_basis_zero_goal': 1,
},
},
'DMP': {},
'ProDMP': {}.
}
[...]
```
If you created a new task wrapper, feel free to open a PR, so we can integrate it for others to use as well. Without the
integration the task can still be used. A rough outline can be shown here, for more details we recommend having a look
at the [examples](https://github.com/ALRhub/fancy_gym/tree/master/fancy_gym/examples/).
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:
@ -304,6 +176,7 @@ fancy_gym.register(
)
```
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