Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos
📄 Read Paper
📣 Blog Post
👾 MineRL Environment (note version 1.0+ required)
🏁 MineRL BASALT Competition
Install pre-requirements for MineRL. Then install the vpt package after CDing into the cloned repo.
cd Video-Pre-Training
pip install -e ./
To run the code, call
python vpt/run_agent.py --model [path to .model file] --weights [path to .weight file]
After loading up, you should see a window of the agent playing Minecraft.
There are also some .sh files prefixed with demo_ inside vpt/scripts for easier access. Make sure you remember to download the model files and their corresponding weights.
Below are the model files and weights files for various pre-trained Minecraft models. The 1x, 2x and 3x model files correspond to their respective model weights width.
These models are trained on video demonstrations of humans playing Minecraft using behavioral cloning (BC) and are more general than later models which use reinforcement learning (RL) to further optimize the policy. Foundational models are trained across all videos in a single training run while house and early game models refine their respective size foundational model further using either the housebuilding contractor data or early game video sub-set. See the paper linked above for more details.
These models further refine the above demonstration based models with a reward function targeted at obtaining diamond pickaxes. While less general then the behavioral cloning models, these models have the benefit of interacting with the environment using a reward function and excel at progressing through the tech tree quickly. See the paper for more information on how they were trained and the exact reward schedule.
IDM aims to predict what actions player is taking in a video recording.
Setup:
- Install requirements:
pip install -r requirements.txt - Download the IDM model .model ⬇️ and .weight ⬇️ files
- For demonstration purposes, you can use the contractor recordings shared below to. For this demo we use this .mp4 and this associated actions file (.jsonl).
To run the model with above files placed in the root directory of this code:
python vpt/run_inverse_dynamics_model.py -weights 4x_idm.weights --model 4x_idm.model --video-path cheeky-cornflower-setter-02e496ce4abb-20220421-092639.mp4 --jsonl-path cheeky-cornflower-setter-02e496ce4abb-20220421-092639.jsonl
A window should pop up which shows the video frame-by-frame, showing the predicted and true (recorded) actions side-by-side on the left.
Note that run_inverse_dynamics_model.py is designed to be a demo of the IDM, not code to put it into practice.
Disclaimer: This code is a rough demonstration only and not an exact recreation of what original VPT paper did (but it contains some preprocessing steps you want to be aware of)! As such, do not expect replicate the original experiments with this code. This code has been designed to be run-able on consumer hardware (e.g., 8GB of VRAM).
Setup:
- Install requirements:
pip install -r requirements.txt - Download
.weightsand.modelfile for model you want to fine-tune. - Download contractor data (below) and place the
.mp4and.jsonlfiles to the same directory (e.g.,data). With default settings, you need at least 12 recordings.
If you downloaded the "1x Width" models and placed some data under data directory, you can perform finetuning with
python vpt/behavioural_cloning.py --data-dir data --in-model foundation-model-1x.model --in-weights foundation-model-1x.weights --out-weights finetuned-1x.weights
You can then use finetuned-1x.weights when running the agent. You can change the training settings at the top of behavioural_cloning.py.
Major limitations:
- Only trains single step at the time, i.e., errors are not propagated through timesteps.
- Computes gradients one sample at a time to keep memory use low, but also slows down the code.
Over the course of the project we requested various demonstrations from contractors which we release as index files below. In general, major recorder versions change for a new prompt or recording feature while bug-fixes were represented as minor version changes. However, some recorder versions we asked contractors to change their username when recording particular modalities. Also, as contractors internally ask questions, clarification from one contractor may result in a behavioral change in the other contractor. It is intractable to share every contractor's view for each version, but we've shared the prompts and major clarifications for each recorder version where the task changed significantly.
Initial Prompt
We are collecting data for training AI models in Minecraft. You'll need to install java, download the modified version of minecraft (that collects and uploads your play data), and play minecraft survival mode! Paid per hour of gameplay. Prior experience in minecraft not. necessary. We do not collect any data that is unrelated to minecraft from your computer.
The following is a list of the available versions:
-
6.x Core recorder features subject to change ⬇️ index file
-
6.9 First feature complete recorder version
-
6.10 Fixes mouse scaling on Mac when gui is open
-
6.11 Tracks the hotbar slot
-
6.13 Sprinting, swap-hands, ... (see commits below)
Commits
- improve replays that are cut in the middle of gui; working on riding boats / replays cut in the middle of a run
- improve replays by adding dwheel action etc, also, loosen up replay tolerances
- opencv version bump
- add swap hands, and recording of the step timestamp
- implement replaying from running and sprinting and tests
- do not record sprinting (can use stats for that)
- check for mouse button number, ignore >2
- handle the errors when mouse / keyboard are recorded as null
-
-
7.x Prompt changes ⬇️ index file
-
7.6 Bump version for internal tracking
Additional ask to contractors
Right now, early game data is especially valuable to us. As such, we request that at least half of the data you upload is from the first 30 minutes of the game. This means that, for every hour of gameplay you spend in an older world, we ask you to play two sessions in which you create a new world and play for 30 minutes. You can play for longer in these worlds, but only the first 30 minutes counts as early game data.
-
-
8.x 📋 House Building from Scratch Task ⬇️ index
Changes and Prompt
Hi all! Thank you for your hard work so far.
This week we would like to have you all collect data on a specific task.
This comes with a new recorder version 8.0 which you will need to update your recording script to download.
This week we would like you to use a new world each time you play, so loading existing worlds is disabled.
The new task is as follows:
Starting in a new world, build a simple house in 10-15 minutes. This corresponds to one day and a bit of the night. Please use primarily wood, dirt, and sand, as well as crafted wood items such as doors, fences, ect. in constructing your house. Avoid using difficult items such as stone. Aside from those constraints, you may decorate the structure you build as you wish. It does not need to have any specific furniture. For example, it is OK if there is no bed in your house. If you have not finished the house by the sunrise (20 minutes) please exit and continue to another demonstration. Please continue to narrate what you are doing while completing this task.
Since you will be unable to resume building after exiting Minecraft or going back to the main menu, you must finish these demonstrations in one session. Pausing via the menu is still supported. If you want to view your creations later, they will be saved locally so you can look at them in your own time. We may use these save files in a future task so if you have space, please leave the save files titled “build-house-15-min-“.
For this week try to avoid all cobblestone / stone / granite
For this week we just want simple houses without sleeping. If 10 minutes is too short, let us know and we can think of how to adjust!
Stone tools are ok but I think you may run-out of time
Changes:
- Timer ends episode after 10 realtime minutes
- Worlds are named:
"build-house-15-min-" + Math.abs(random.nextInt());
- Note this version introduces 10-minute timer that ends the episode. It cut experiments short occasionally and was fixed in 9.1
- 8.0 Simple House
- 8.2 Update upload script
-
9.x 📋 House Building from Random Starting Materials Task ⬇️ index
Changes and Prompt
You now will have 10 minutes to use the provided resources to build your house / home / or structure. In this version, the experiment will time out after 10 minutes if you are not complete so don't be alarmed if that happens, it is intentional.
No need to use up all the resources! It's ok to collect a few things but spend the majority of the time placing blocks (the act of placing seems to be harder to learn)
Changes:
- Worlds are named:
"design-house-10-min-" + Math.abs(random.nextInt()); - Starting inventory given by code below
Random Starting Inventory Code
Random random = new Random(); List<ItemStack> hotbar = new ArrayList<>(); List<ItemStack> inventory = new ArrayList<>(); // Ensure we give the player the basic tools in their hot bar hotbar.add(new ItemStack(Items.STONE_AXE)); hotbar.add(new ItemStack(Items.STONE_PICKAXE)); hotbar.add(new ItemStack(Items.STONE_SHOVEL)); hotbar.add(new ItemStack(Items.CRAFTING_TABLE)); // Add some random items to the player hotbar as well addToList(hotbar, inventory, Items.TORCH, random.nextInt(16) * 2 + 2); // Next add main building blocks if (random.nextFloat() < 0.7) { addToList(hotbar, inventory, Items.OAK_FENCE_GATE, random.nextInt(5)); addToList(hotbar, inventory, Items.OAK_FENCE, random.nextInt(5) * 64); addToList(hotbar, inventory, Items.OAK_DOOR, random.nextInt(5)); addToList(hotbar, inventory, Items.OAK_TRAPDOOR, random.nextInt(2) * 2); addToList(hotbar, inventory, Items.OAK_PLANKS, random.nextInt(3) * 64 + 128); addToList(hotbar, inventory, Items.OAK_SLAB, random.nextInt(3) * 64); addToList(hotbar, inventory, Items.OAK_STAIRS, random.nextInt(3) * 64); addToList(hotbar, inventory, Items.OAK_LOG, random.nextInt(2) * 32); addToList(hotbar, inventory, Items.OAK_PRESSURE_PLATE, random.nextInt(5)); } else { addToList(hotbar, inventory, Items.BIRCH_FENCE_GATE, random.nextInt(5)); addToList(hotbar, inventory, Items.BIRCH_FENCE, random.nextInt(5) * 64); addToList(hotbar, inventory, Items.BIRCH_DOOR, random.nextInt(5)); addToList(hotbar, inventory, Items.BIRCH_TRAPDOOR, random.nextInt(2) * 2); addToList(hotbar, inventory, Items.BIRCH_PLANKS, random.nextInt(3) * 64 + 128); addToList(hotbar, inventory, Items.BIRCH_SLAB, random.nextInt(3) * 64); addToList(hotbar, inventory, Items.BIRCH_STAIRS, random.nextInt(3) * 64); addToList(hotbar, inventory, Items.BIRCH_LOG, random.nextInt(2) * 32); addToList(hotbar, inventory, Items.BIRCH_PRESSURE_PLATE, random.nextInt(5)); } // Now add some random decoration items to the player inventory addToList(hotbar, inventory, Items.CHEST, random.nextInt(3)); addToList(hotbar, inventory, Items.FURNACE, random.nextInt(2) + 1); addToList(hotbar, inventory, Items.GLASS_PANE, random.nextInt(5) * 4); addToList(hotbar, inventory, Items.WHITE_BED, (int) (random.nextFloat() + 0.2)); // Bed 20% of the time addToList(hotbar, inventory, Items.PAINTING, (int) (random.nextFloat() + 0.1)); // Painting 10% of the time addToList(hotbar, inventory, Items.FLOWER_POT, (int) (random.nextFloat() + 0.1) * 4); // 4 Flower pots 10% of the time addToList(hotbar, inventory, Items.OXEYE_DAISY, (int) (random.nextFloat() + 0.1) * 4); // 4 Oxeye daisies 10% of the time addToList(hotbar, inventory, Items.POPPY, (int) (random.nextFloat() + 0.1) * 4); // 4 Poppies 10% of the time addToList(hotbar, inventory, Items.SUNFLOWER, (int) (random.nextFloat() + 0.1) * 4); // 4 Sunflowers 10% of the time // Shuffle the hotbar slots and inventory slots Collections.shuffle(hotbar); Collections.shuffle(inventory); // Give the player the items this.mc.getIntegratedServer().getPlayerList().getPlayers().forEach(p -> { if (p.getUniqueID().equals(this.getUniqueID())) { hotbar.forEach(p.inventory::addItemStackToInventory); inventory.forEach(p.inventory::addItemStackToInventory); } });
- 9.0 First version
- 9.1 Fixed timer bug
- Worlds are named:
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10.0 📋 Obtain Diamond Pickaxe Task ⬇️ index
Changes and Prompt
Prompt:For this new task we have given you 20 minutes to craft a diamond pickaxe. We ask that you do not try to search for villages or other ways of getting diamonds, but if you are spawned in view of one, or happen to fall into a cave structure feel free to explore it for diamonds. If 20 min is not enough that is OK. It will happen on some seeds because of bad luck. Please do not use glitches to find the diamonds.
Changes:
- change to 20 minute time limit
- don't count gui time as part of the time limit
- World are named
"collect-diamond-pickaxe-15min-" + Math.abs(random.nextInt());
Sometimes we asked the contractors to signify other tasks besides changing the version. This primarily occurred in versions 6 and 7 as 8, 9 and 10 are all task specific.
Prompt to contractors (click to show)
Another request about additional time - please use some of it to chop trees. Specifically, please start the recorder by adding --username treechop argument to the script (i.e. use play --username treechop on windows, ./play.sh --username treechop on osx/linux), and spend some time chopping trees! Getting wooden or stone tools is ok, but please spend the majority of the with username treechop specifically chopping. I did it myself for about 15 minutes, and it does get boring pretty quickly, so I don't expect you to do it all the time, but please do at least a little bit of chopping. Feel free to play normally the rest of the time (but please restart without --username treechop argument when you are not chopping) However, it is preferable that you start a new world though, and use only the tools that are easily obtainable in that world. I'll see what I can do about getting player an iron axe - that sounds reasonable, and should not be hard, but will require a code update.We restrict the contractors to playing Minecraft in windowed mode at 720p which we downsample at 20hz to 360p to minimize space. We also disabled the options screen to prevent the contractor from changing things such as brightness, or rendering options. We ask contractors not to press keys such as f3 which shows a debug overlay, however some contractors may still do this.
Demonstrations are broken up into up to 5 minute segments consisting of a series of compressed screen observations, actions, environment statistics, and a checkpoint save file from the start of the segment. Each relative path in the index will have all the files for that given segment, however if a file was dropped while uploading, the corresponding relative path is not included in the index therefore there may be missing chunks from otherwise continuous demonstrations.
Index files are provided for each version as a json file:
{
"basedir": "https://openaipublic.blob.core.windows.net/data/",
"relpaths": [
"8.0/cheeky-cornflower-setter-74ae6c2eae2e-20220315-122354",
...
]
}Relative paths follow the following format:
<recorder-version>/<contractor-alias>-<session-id>-<date>-<time>
Note that due to network errors, some segments may be missing from otherwise continuous demonstrations.
Your data loader can then find following files:
- Video observation:
<basedir>/<relpath>.mp4 - Action file:
<basedir>/<relpath>.jsonl - Options file:
<basedir>/<relpath>-options.json - Checkpoint save file:
<basedir>/<relpath>.zip
The action file is not a valid json object: each line in action file is an individual action dictionary.
For v7.x, the actions are in form
{
"mouse": {
"x": 274.0,
"y": 338.0,
"dx": 0.0,
"dy": 0.0,
"scaledX": -366.0,
"scaledY": -22.0,
"dwheel": 0.0,
"buttons": [],
"newButtons": []
},
"keyboard": {
"keys": [
"key.keyboard.a",
"key.keyboard.s"
],
"newKeys": [],
"chars": ""
},
"isGuiOpen": false,
"isGuiInventory": false,
"hotbar": 4,
"yaw": -112.35006,
"pitch": 8.099996,
"xpos": 841.364694513396,
"ypos": 63.0,
"zpos": 24.956354839537802,
"tick": 0,
"milli": 1649575088006,
"inventory": [
{
"type": "oak_door",
"quantity": 3
},
{
"type": "oak_planks",
"quantity": 59
},
{
"type": "stone_pickaxe",
"quantity": 1
},
{
"type": "oak_planks",
"quantity": 64
}
],
"serverTick": 6001,
"serverTickDurationMs": 36.3466,
"stats": {
"minecraft.custom:minecraft.jump": 4,
"minecraft.custom:minecraft.time_since_rest": 5999,
"minecraft.custom:minecraft.play_one_minute": 5999,
"minecraft.custom:minecraft.time_since_death": 5999,
"minecraft.custom:minecraft.walk_one_cm": 7554,
"minecraft.use_item:minecraft.oak_planks": 5,
"minecraft.custom:minecraft.fall_one_cm": 269,
"minecraft.use_item:minecraft.glass_pane": 3
}
}This was a large effort by a dedicated team at OpenAI: Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, Jeff Clune The code here represents a minimal version of our model code which was prepared by Anssi Kanervisto and others so that these models could be used as part of the MineRL BASALT competition.