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Install MineDojo environment

Warning

The Categorical PyTorch distribution does not work correctly when some probabilities are set to $0$ (the probs set to zero can be sampled anyway). This can cause some errors when filtering actions with the action masks provided by MineDojo. This error is sporadic, so, we are waiting for the issue to be fixed by PyTorch. One can track the issue here: pytorch/pytorch#91863

First you need to install the JDK 1.8, on Debian based systems you can run the following:

sudo apt update -y
sudo apt install -y software-properties-common
sudo add-apt-repository ppa:openjdk-r/ppa
sudo apt update -y
sudo apt install -y openjdk-8-jdk
sudo update-alternatives --config java

Note

If you work on another OS, you can follow the instructions here to install JDK 1.8.

Now, you can install the MineDojo environment:

pip install -e .[minedojo]

Warning

If you run into any problems during the installation due to some missing files that are not downloaded, please have a look at this issue.

MineDojo environments

Note

So far, you can run an experiment with the MineDojo environments only with the Dreamers' agents.

It is possible to train your agents on all the tasks provided by MineDojo. You need to select the MineDojo environment (env=minedojo) and set the env.id to the name of the task on which you want to train your agent. Moreover, you have to specify the class of the MinedojoActor (algo.actor.cls=sheeprl.algos.<algo_name>.agent.MinedojoActor). For instance, you can use the following command to select the MineDojo open-ended environment.

python sheeprl.py exp=p2e_dv2 env=minedojo env.id=open-ended algo.actor.cls=sheeprl.algos.p2e_dv2.agent.MinedojoActor algo.cnn_keys.encoder=[rgb]

Observation Space

We slightly modified the observation space, by reshaping it (based on the idea proposed by Hafner in DreamerV3):

  • We represent the inventory with a vector with one entry for each item of the game which gives the quantity of the corresponding item in the inventory.
  • A max inventory vector with one entry for each item which contains the maximum number of items obtained by the agent so far in the episode.
  • A delta inventory vector with one entry for each item which contains the difference of the items in the inventory after the performed action.
  • The RGB first-person camera image.
  • A vector of three elements representing the life, the food, and the oxygen levels of the agent.
  • A one-hot vector indicating the equipped item.
  • A mask for the action type indicating which actions can be executed.
  • A mask for the equip/place arguments indicating which elements can be equipped or placed.
  • A mask for the destroy arguments indicating which items can be destroyed.
  • A mask for craft smelt indicating which items can be crafted.

For more information about the MineDojo observation space, check here.

Action Space

We decided to convert the 8 multi-discrete action space into a 3 multi-discrete action space:

  1. The first maps all the actions (movement, craft, jump, camera, attack, ...).
  2. The second one maps the argument for the craft action.
  3. The third one maps the argument for the equip, place, and destroy actions.

Moreover, we restrict the look-up/down actions between min_pitch and max_pitch degrees, where min_pitch and max_pitch are two parameters that can be defined through the env.min_pitch and env.max_pitch cli arguments, respectively. In addition, we added the forward action when the agent selects one of the following actions: jump, sprint, and sneak. Finally, we added sticky actions for the jump and attack actions. You can set the values of the sticky_jump and sticky_attack parameters through the env.sticky_jump and env.sticky_attack cli arguments, respectively. The sticky actions, if set, force the agent to repeat the selected actions for a certain number of steps.

Note

The env.sticky_attack parameter is set to 0 if the env.break_speed_multiplier > 1.

For more information about the MineDojo action space, check here.

Note

Since the MineDojo environments have a multi-discrete action space, the sticky actions can be easily implemented. The agent will perform the selected action and the sticky actions simultaneously.

The action repeat in the Minecraft environments is set to 1, indeed, It makes no sense to force the agent to repeat an action such as crafting (it may not have enough material for the second action).

Headless machines

If you work on a headless machine, you need to software renderer. We recommend to adopt one of the following solutions:

  1. Install the xvfb software with the sudo apt install xvfb command and prefix the training command with xvfb-run. For instance, to train DreamerV2 on the navigate task on a headless machine, you need to run the following command: xvfb-run python sheeprl.py exp=p2e_dv2 fabric.devices=1 env=minedojo env.id=open-ended algo.cnn_keys.encoder=[rgb] algo.actor.cls=sheeprl.algos.p2e_dv2.agent.MinedojoActor, or MINEDOJO_HEADLESS=1 python sheeprl.py exp=p2e_dv2 fabric.devices=1 env=minedojo env.id=open-ended algo.cnn_keys.encoder=[rgb] algo.actor.cls=sheeprl.algos.p2e_dv2.agent.MinedojoActor.
  2. Exploit the PyVirtualDisplay package.