57 lines
1.9 KiB
Markdown
57 lines
1.9 KiB
Markdown
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# DeepMind Control Suite.
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This submodule contains the domains and tasks described in the
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[DeepMind Control Suite tech report](https://arxiv.org/abs/1801.00690).
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## Quickstart
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```python
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from dm_control import suite
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import numpy as np
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# Load one task:
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env = suite.load(domain_name="cartpole", task_name="swingup")
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# Iterate over a task set:
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for domain_name, task_name in suite.BENCHMARKING:
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env = suite.load(domain_name, task_name)
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# Step through an episode and print out reward, discount and observation.
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action_spec = env.action_spec()
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time_step = env.reset()
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while not time_step.last():
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action = np.random.uniform(action_spec.minimum,
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action_spec.maximum,
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size=action_spec.shape)
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time_step = env.step(action)
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print(time_step.reward, time_step.discount, time_step.observation)
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```
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## Illustration video
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Below is a video montage of solved Control Suite tasks, with reward
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visualisation enabled.
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[](https://www.youtube.com/watch?v=rAai4QzcYbs)
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### Quadruped domain [April 2019]
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Roughly based on the 'ant' model introduced by [Schulman et al. 2015](https://arxiv.org/abs/1506.02438). Main modifications to the body are:
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- 4 DoFs per leg, 1 constraining tendon.
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- 3 actuators per leg: 'yaw', 'lift', 'extend'.
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- Filtered position actuators with timescale of 100ms.
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- Sensors include an IMU, force/torque sensors, and rangefinders.
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Four tasks:
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- `walk` and `run`: self-right the body then move forward at a desired speed.
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- `escape`: escape a bowl-shaped random terrain (uses rangefinders).
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- `fetch`, go to a moving ball and bring it to a target.
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All behaviors in the video below were trained with [Abdolmaleki et al's
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MPO](https://arxiv.org/abs/1806.06920).
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[](https://www.youtube.com/watch?v=RhRLjbb7pBE)
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