crafter 1.5.1

Open world survival game for reinforcement learning.

Crafter

Open world survival environment for reinforcement learning.

If you find this code useful, please reference in your paper:

@misc{hafner2021crafter,
  title = {Crafter: An Open World Survival Benchmark},
  author = {Danijar Hafner},
  year = {2021},
  howpublished = {\url{https://github.com/danijar/crafter}},
}

Highlights

Crafter is a simulated environment that tests a variety of general abilities of learning agents. It features a randomized open-ended world with image inputs where the player discovers resources and tools, all while ensuring its own survival.

• Generalization: New procedurally generated map for each episode.
• Exploration: Materials unlock new tools which in turn unlock new materials.
• Long dependencies: Gathering resources, building shelter, growing fruits.
• Partial observability: Each input image reveals only a small part of the world.
• Survival: Must find food and water, shelter to rest, defend against monsters.
• Easy to use: Pure Python, windowless rendering, few dependencies, flat action space.

Status

Crafter has been released in its stable version v1.x.x and can now be used for research projects. The environment will not change anymore to ensure comparability. Future changes may add additional debugging features that do not affect the environment itself.

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 pygame
pip3 install crafter

# Start the game
crafter

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

The following optional command line flags are available:

Flag	Default	Description
--record <directory>	None	Directory for recording trajectories in NPZ and MP4 formats.
--window <width> <height>	600 600	Window size in pixels.
--area <width> <height>	64 64	The number of grid cells of the generated world.
--view <width> <height>	9 9	The number of grid cells that are visible in the images.
--size <width> <height>	0 0	Render resolution; defaults to the window size. Setting this to 64 64 shows the low resolution graphics that artificial agents see.
--wait <boolean>	False	Pauses the game while the player chooses their action.
--seed <integer>	None	Determines world generation and creatures.

Training Agents

Installation: pip3 install -U crafter

The environment follows the OpenAI Gym interface:

import crafter

env = crafter.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

Constructor

To ensure comparability across research papers, we recommend using the environment in its default configuration. Nonetheless, the environment can be configured via its constructor:

crafter.Env(area=(64, 64), view=(9, 9), size=(64, 64), length=10000, seed=None)

Parameter	Default	Description
area	(64, 64)	Size of the world in grid cells.
view	(9, 9)	Layout size in cells; determines view distance.
size	(64, 64)	Render size of the images in pixels.
length	10000	Time limit for the episode, can be None.
seed	None	Interger that determines world generation and creatures.

Reward

The reward can either be given to the agent or used as a proxy metric for evaluating unsupervised agents.

The reward is +1 when the agent unlocks a new achievement, -0.1 when its health level decreases, +0.1 when it increases, and 0 for all other time steps. The achievements are as follows:

• collect_coal
• collect_diamond
• collect_drink
• collect_iron
• collect_sapling
• collect_stone
• collect_wood
• defeat_skeleton
• defeat_zombie
• eat_cow
• eat_plant
• make_iron_pickaxe
• make_iron_sword
• make_stone_pickaxe
• make_stone_sword
• make_wood_pickaxe
• make_wood_sword
• place_furnace
• place_plant
• place_stone
• place_table
• wake_up

The sum of rewards per episode can range from -0.9 (losing all health without any achievements) to 22 (unlocking all achievements and keeping or restoring all health until the time limit is reached). A score of 21.1 or higher means that all achievements have been unlocked.

Termination

The episode terminates when the health points of the agent reach zero. Episodes also end when reaching a time limit, which is 10000 steps by default.

Observation Space

Each observation is an RGB image that shows a local view of the world around the player, as well as the life statistics and 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	Requirement
0	noop	Always applicable.
1	move_left	Flat ground left to the agent.
2	move_right	Flat ground right to the agent.
3	move_up	Flat ground above the agent.
4	move_down	Flat ground below the agent.
5	do	Facing creature or material and have necessary tool.
6	sleep	Energy level is below maximum.
7	place_stone	Stone in inventory.
8	place_table	Wood in inventory.
9	place_furnace	Stone in inventory.
10	place_plant	Sapling in inventory.
11	make_wood_pickaxe	Nearby table. Wood in inventory.
12	make_stone_pickaxe	Nearby table. Wood, stone in inventory.
13	make_iron_pickaxe	Nearby table, furnace. Wood, coal, iron an inventory.
14	make_wood_sword	Nearby table. Wood in inventory.
15	make_stone_sword	Nearby table. Wood, stone in inventory.
16	make_iron_sword	Nearby table, furnace. Wood, coal, iron an inventory.

Info Dictionary

The step function returns an info directionary with additional information about the environment state. It can be used for evaluation and debugging but should not be provided to the agent. The following entries are available:

Key	Type	Description
inventory	dict	Mapping from item names to inventory counts.
achievements	dict	Mapping from achievement names to their counts.
discount	float	1 during the episode and 0 at the last step.
semantic	np.array	Categorical representation of the world.
player_pos	tuple	X and Y position of the player in the world.

Baselines

Crafter is designed to be challenging for current learning algorithms but not completely out of reach. To verify how challenging the environment is, we trained the DreamerV2 agent on Crafter with rewards. We recommend training for 5M environment steps and reporting the mean score. During this time, the agent makes consistent learning progress.

When training over 10 times longer, the agent also rarely unlocks all achievements during an episode, including finding a diamond. The open research challenge ahead of us is to drastically accelerate the exploration and learning progress and increase the average score by consistently unlocking all achievements.

Questions

Please open an issue on Github.