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Open world survival game for reinforcement learning.

Project description

Crafter

PyPI

Open world survival environment for reinforcement learning.

Crafter Terrain

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 procedurally generated 2D world, where the agent finds food, and collect materials to build tools, which in turn unlock new materials, all while avoiding or defending against monsters.

  • Generalization: New procedurally generated map for each episode.
  • Exploration: Materials unlock new tools which in turn unlock new materials.
  • Memory: Input images show small part of the world centered at the agent.
  • Survival: Must find food and avoid or defend against creatures.
  • Reproducible: All randomness is controlled by a single seed.
  • Easy to use: Image inputs and flat categorical action space.

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 'crafter[gui]'

# Start the game
crafter

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

Crafter Video

The following optional command line flags are available:

Flag Default Description
--window <width> <height> 1280 720 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.
--area <width> <height> 64 64 The size of the world in cells.
--view <width> <height> 23 13 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 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

For comparability between 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.
health 5 Initial health level of the player.
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_iron
  • collect_stone
  • collect_tree
  • defeat_zombie
  • defeat_skeleton
  • find_food
  • make_iron_pickaxe
  • make_stone_pickaxe
  • make_wood_pickaxe
  • make_iron_sword
  • make_stone_sword
  • make_wood_sword
  • place_furnace
  • place_stone
  • place_table

The sum of rewards per episode can range from -0.5 (losing all health without any achievements) to 17 (unlocking all achievements and keeping or restoring all health).

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 health counter 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 place_stone Stone in inventory.
7 place_table Wood in inventory.
8 place_furnace Stone in inventory.
9 make_wood_pickaxe Nearby table. Wood in inventory.
10 make_stone_pickaxe Nearby table. Wood, stone in inventory.
11 make_iron_pickaxe Nearby table, furnace. Wood, coal, iron an inventory.
12 make_wood_sword Nearby table. Wood in inventory.
13 make_stone_sword Nearby table. Wood, stone in inventory.
14 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
health uint8 Health counter of the player.
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.

Baselines

To understand how challenging the environment is, we trained the DreamerV2 agent 10 times for 30M environment steps each. The agent receives the rewards that correspond to the 13 achievements that can be unlocked in each episode, the most difficult of which is to collect a diamond.

Crafter Terrain

We observe consistent learning progress. Eventually, many of the runs sporadically collect a diamond. This shows that the environment is challenging and unsolved but not completely out of reach.

Questions

Please open an issue on Github.

Project details


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crafter-0.24.2.tar.gz (102.8 kB view hashes)

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