Metadata-Version: 2.1
Name: crafter
Version: 0.26.0
Summary: Open world survival game for reinforcement learning.
Home-page: http://github.com/danijar/crafter
License: UNKNOWN
Description: # Crafter
        
        [![PyPI](https://img.shields.io/pypi/v/crafter.svg)](https://pypi.python.org/pypi/crafter/#history)
        
        Open world survival environment for reinforcement learning.
        
        ![Crafter Terrain](https://github.com/danijar/crafter/raw/main/media/terrain.png)
        
        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 complex simulated environment that tests a variety of abilities of
        learning agents. It features an 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.
        - **Partial observability:** Each input image reveals only a small part of the world.
        - **Survival:** Must find food and water, rest, and defend against monsters.
        - **Easy to use:** Pure Python, image inputs, flat categorical actions.
        
        ## 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.
        
        ```sh
        # Install with GUI
        pip3 install 'crafter[gui]'
        
        # Start the game
        crafter
        
        # Alternative way to start the game
        python3 -m crafter.run_gui
        ```
        
        ![Crafter Video](https://i.imgur.com/S17T3W7.gif)
        
        The following optional command line flags are available:
        
        | Flag | Default | Description |
        | :--- | :-----: | :---------- |
        | `--record <filename>.mp4` | None | Record a video of the trajectory. |
        | `--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. |
        | `--seed <integer>` | None | Determines world generation and creatures. |
        
        ## Training Agents
        
        Installation: `pip3 install -U crafter`
        
        The environment follows the [OpenAI Gym][gym] interface:
        
        ```py
        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)
        ```
        
        [gym]: https://github.com/openai/gym
        
        ## 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:
        
        ```py
        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`
        
        The sum of rewards per episode can range from -0.9 (losing all health without
        any achievements) to 21 (unlocking all achievements and keeping or restoring
        all health until the time limit is reached). A score of 20.1 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. |
        
        ## Baselines
        
        To understand how challenging the environment is, we trained the
        [DreamerV2][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](https://github.com/danijar/crafter/raw/main/media/dreamerv2.png)
        
        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.
        
        [dreamerv2]: https://github.com/danijar/dreamerv2
        
        ## Questions
        
        Please [open an issue][issues] on Github.
        
        [issues]: https://github.com/danijar/crafter/issues
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Games/Entertainment
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
Provides-Extra: gui
