https://github.com/instance01/gym-mini-envs
Small minimum size gym environments for testing (and maybe finding edge cases)
https://github.com/instance01/gym-mini-envs
gym-env gym-environments
Last synced: 3 months ago
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Small minimum size gym environments for testing (and maybe finding edge cases)
- Host: GitHub
- URL: https://github.com/instance01/gym-mini-envs
- Owner: instance01
- License: mit
- Created: 2020-05-25T11:01:24.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-06-07T04:27:31.000Z (almost 5 years ago)
- Last Synced: 2023-12-16T16:14:16.060Z (over 1 year ago)
- Topics: gym-env, gym-environments
- Language: Python
- Size: 4.88 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
This is a collection of small environments that aid in developing new Machine Learning methods.
They may also help in finding edge cases. If a method works on hard environments but not on simple ones, something may be wrong.
Currently there are two simple environments:
* `mini_discrete_env`: Start at position 0. Go to the right until position x. Then go to the left until position y. Reward is 100 for finishing minus a certain factor that takes into account the number of actions used. Returned observation is the current position and the global state (whether the agent has visited position x yet).
* `mini_continuous_env`: Same as `mini_discrete_env`, but with continuous action/observation space.
Example:
```
import gym
import mini_envs.discrete_envs.mini_discrete_env
env = gym.make('MiniDiscreteEnv-v0')
# The following lines are optional customizations.
env.goal_pos = [3, -3] # In accordance to above description, first one is x, second one is y.
env.borders = [7, -7] # Agent won't be able to go beyond borders.
env.max_steps = 100
obs, reward, done, _ = env.step(0) # Go to the left.
obs, reward, done, _ = env.step(1) # Go to the right.
```
Install:
`pip3 install -e .`, or:
`pip3 install git+git://github.com/instance01/gym-mini-envs.git`