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## ALR Environments
## ALR Robotics Control Environments
This repository collects custom Robotics environments not included in benchmark suites like OpenAI gym, rllab, etc.
Creating a custom (Mujoco) gym environment can be done according to [this guide](https://github.com/openai/gym/blob/master/docs/creating-environments.md).
For stochastic search problems with gym interface use the `Rosenbrock-v0` reference implementation.
We also support to solve environments with Dynamic Movement Primitives (DMPs) and Probabilistic Movement Primitives (DetPMP, we only consider the mean usually).
When adding new DMP tasks check the `ViaPointReacherDMP-v0` reference implementation.
When simply using the tasks, you can also leverage the wrapper class `DmpWrapper` to turn normal gym environments in to DMP tasks.
## Environments
## Step-based Environments
Currently we have the following environments:
### Mujoco
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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 | 5 | 18
|`HoleReacher-v0`| 5 link reaching task where the end-effector needs to reach into a narrow hole without collding with itself or walls | 200 | 5 | 18
### DMP Environments
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.
The goal is to learn the parameters of this DMP to generate a suitable trajectory.
All environments provide the full episode reward and additional information about early terminations, e.g. due to collisions.
## Motion Primitive Environments (Episodic environments)
Unlike step-based environments, these motion primitive (MP) environments are closer to stochastic search and what can be found in robotics. They always execute a full trajectory, which is computed by a Dynamic Motion Primitive (DMP) or Probabilitic Motion Primitive (DetPMP) and translated into individual actions with a controller, e.g. a PD controller. The actual Controller, however, depends on the type of environment, i.e. position, velocity, or torque controlled.
The goal is to learn the parametrization of the motion primitives in order to generate a suitable trajectory.
MP This can also be done in a contextual setting, where all changing elements of the task are exposed once in the beginning. This requires to find a new parametrization for each trajectory.
All environments provide the full cumulative episode reward and additional information about early terminations, e.g. due to collisions.
### Classic Control
|Name| Description|Horizon|Action Dimension|Context Dimension
|---|---|---|---|---|
|`ViaPointReacherDMP-v0`| A DMP provides a trajectory for the `ViaPointReacher-v0` task. | 200 | 25
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[//]: |`HoleReacherDetPMP-v0`|
### OpenAi-gym Environments
These environments are wrapped-versions of their OpenAi-gym counterparts.
### OpenAI gym Environments
These environments are wrapped-versions of their OpenAI-gym counterparts.
|Name| Description|Horizon|Action Dimension|Context Dimension
|Name| Description|Trajectory Horizon|Action Dimension|Context Dimension
|---|---|---|---|---|
|`ContinuousMountainCarDetPMP-v0`| A DetPmP wrapped version of the ContinuousMountainCar-v0 environment. | 100 | 1
|`ReacherDetPMP-v2`| A DetPmP wrapped version of the Reacher-v2 environment. | 50 | 2
|`FetchSlideDenseDetPMP-v1`| A DetPmP wrapped version of the FetchSlideDense-v1 environment. | 50 | 4
|`FetchReachDenseDetPMP-v1`| A DetPmP wrapped version of the FetchReachDense-v1 environment. | 50 | 4
### Deep Mind Control Suite Environments
These environments are wrapped-versions of their Deep Mind Control Suite (DMC) counterparts.
Given most task can be solved in shorter horizon lengths than the original 1000 steps, we often shorten the episodes for those task.
### Stochastic Search
|Name| Description|Trajectory Horizon|Action Dimension|Context Dimension
|---|---|---|---|---|
|`dmc_ball_in_cup-catch_detpmp-v0`| A DetPmP wrapped version of the "catch" task for the "ball_in_cup" environment. | 50 | 10 | 2
|`dmc_ball_in_cup-catch_dmp-v0`| A DMP wrapped version of the "catch" task for the "ball_in_cup" environment. | 50| 10 | 2
|`dmc_reacher-easy_detpmp-v0`| A DetPmP wrapped version of the "easy" task for the "reacher" environment. | 1000 | 10 | 4
|`dmc_reacher-easy_dmp-v0`| A DMP wrapped version of the "easy" task for the "reacher" environment. | 1000| 10 | 4
|`dmc_reacher-hard_detpmp-v0`| A DetPmP wrapped version of the "hard" task for the "reacher" environment.| 1000 | 10 | 4
|`dmc_reacher-hard_dmp-v0`| A DMP wrapped version of the "hard" task for the "reacher" environment. | 1000 | 10 | 4
## Stochastic Search
|Name| Description|Horizon|Action Dimension|Observation Dimension
|---|---|---|---|---|
|`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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```
For an example using a DMP wrapped env and asynchronous sampling look at [mp_env_async_sampler.py](./alr_envs/utils/mp_env_async_sampler.py)
For an example using a DMP wrapped env and asynchronous sampling look at [mp_env_async_sampler.py](./alr_envs/utils/mp_env_async_sampler.py)