Partially-observed visual reinforcement learning domain

CowHerd

CowHerd is a partially-observed reinforcement learning environment, where the player walks around an area and is rewarded for milking cows. The cows try to escape and the player can place fences to help capture them. The implementation of CowHerd is based on the Crafter environment.

video

Play Yourself

You can play the game yourself with an interactive window and keyboard input.

The mapping from keys to actions, health level, and inventory state are printed

to the terminal.

# Install with GUI
pip3 install 'cowherd[gui]'

# Start the game
cowherd

# Alternative way to start the game
python3 -m cowherd.run_gui

The following optional command line flags are available:

Flag	Default	Description
`--window <width> <height>`	800 800	Window size in pixels, used as width and height.
`--fps <integer>`	5	How many times to update the environment per second.
`--record <filename>.mp4`	None	Record a video of the trajectory.
`--num_cows`	3	The number of cows in the environment.
`--view <width> <height>`	7 7	The layout size in cells; determines view distance.
`--length <integer>`	None	Time limit for the episode.
`--seed <integer>`	None	Determines world generation and creatures.

Training Agents

Installation: pip3 install -U cowherd

The environment follows the OpenAI Gym interface:

import cowherd

env = cowherd.Env(seed=0)
obs = env.reset()
assert obs.shape == (64, 64, 3)

done = False
while not done:
  action = env.action_space.sample()
  obs, reward, done, info = env.step(action)

Environment Details

Reward

A reward of +1 is given every time the player milks one of the cows.

Termination

Episodes terminate after 1000 steps.

Observation Space

Each observation is an RGB image that shows a local view of the world around

the player, as well as the inventory state of the agent.

Action Space

The action space is categorical. Each action is an integer index representing

one of the possible actions:

Integer	Name	Description
0	`noop`	Do nothing.
1	`move_left`	Walk to the left.
2	`move_right`	Walk to the right.
3	`move_up`	Walk upwards.
4	`move_down`	Walk downwards.
5	`do`	Pick up a placed fence or milk a cow.
6	`place_fence`	Place a fence in front of the player.

GitHub

https://github.com/danijar/cowherd

Source: https://pythonawesome.com/partially-observed-visual-reinforcement-learning-domain/

Games