79 lines
3.8 KiB
Markdown
79 lines
3.8 KiB
Markdown
## ALR Environments
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This repository collects custom Robotics environments not included in benchmark suites like OpenAI gym, rllab, etc.
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Creating a custom (Mujoco) gym environment can be done according to [this guide](https://github.com/openai/gym/blob/master/docs/creating-environments.md).
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For stochastic search problems with gym interface use the `Rosenbrock-v0` reference implementation.
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We also support to solve environments with DMPs. When adding new DMP tasks check the `ViaPointReacherDMP-v0` reference implementation.
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When simply using the tasks, you can also leverage the wrapper class `DmpWrapper` to turn normal gym environments in to DMP tasks.
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## Environments
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Currently we have the following environments:
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### Mujoco
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|Name| Description|Horizon|Action Dimension|Observation Dimension
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|`ALRReacher-v0`|Modified (5 links) Mujoco gym's `Reacher-v2` (2 links)| 200 | 5 | 21
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|`ALRReacherSparse-v0`|Same as `ALRReacher-v0`, but the distance penalty is only provided in the last time step.| 200 | 5 | 21
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|`ALRReacherSparseBalanced-v0`|Same as `ALRReacherSparse-v0`, but the end-effector has to remain upright.| 200 | 5 | 21
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|`ALRLongReacher-v0`|Modified (7 links) Mujoco gym's `Reacher-v2` (2 links)| 200 | 7 | 27
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|`ALRLongReacherSparse-v0`|Same as `ALRLongReacher-v0`, but the distance penalty is only provided in the last time step.| 200 | 7 | 27
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|`ALRLongReacherSparseBalanced-v0`|Same as `ALRLongReacherSparse-v0`, but the end-effector has to remain upright.| 200 | 7 | 27
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### Classic Control
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|Name| Description|Horizon|Action Dimension|Observation Dimension
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|`SimpleReacher-v0`| Simple reaching task (2 links) without any physics simulation. Provides no reward until 150 time steps. This allows the agent to explore the space, but requires precise actions towards the end of the trajectory.| 200 | 2 | 9
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|`LongSimpleReacher-v0`| Simple reaching task (5 links) without any physics simulation. Provides no reward until 150 time steps. This allows the agent to explore the space, but requires precise actions towards the end of the trajectory.| 200 | 5 | 18
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|`ViaPointReacher-v0`| Simple reaching task leveraging a via point, which supports self collision detection. Provides a reward only at 100 and 199 for reaching the viapoint and goal point, respectively.| 200 |
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|`HoleReacher-v0`|
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### DMP Environments
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These environments are closer to stochastic search. They always execute a full trajectory, which is computed by a DMP and executed by a controller, e.g. a PD controller.
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The goal is to learn the parameters of this DMP to generate a suitable trajectory.
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All environments provide the full episode reward and additional information about early terminations, e.g. due to collisions.
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|Name| Description|Horizon|Action Dimension|Observation Dimension
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|`ViaPointReacherDMP-v0`| Simple reaching task leveraging a via point, which supports self collision detection. Provides a reward only at 100 and 199 for reaching the viapoint and goal point, respectively.| 200 |
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|`HoleReacherDMP-v0`|
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|`HoleReacherFixedGoalDMP-v0`|
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|`HoleReacherDetPMP-v0`|
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### Stochastic Search
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|Name| Description|Horizon|Action Dimension|Observation Dimension
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|`Rosenbrock{dim}-v0`| Gym interface for Rosenbrock function. `{dim}` is one of 5, 10, 25, 50 or 100. | 1 | `{dim}` | 0
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## Install
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1. Clone the repository
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```bash
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git clone git@github.com:ALRhub/alr_envs.git
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```
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2. Go to the folder
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```bash
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cd alr_envs
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```
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3. Install with
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```bash
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pip install -e .
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```
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4. Use (see [example.py](./example.py)):
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```python
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import gym
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env = gym.make('alr_envs:SimpleReacher-v0')
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state = env.reset()
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for i in range(10000):
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state, reward, done, info = env.step(env.action_space.sample())
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if i % 5 == 0:
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env.render()
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if done:
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state = env.reset()
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```
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