do not record sprinting (can use stats for that).implement replaying from running and sprinting and tests.add swap hands, and recording of the step timestamp.improve replays by adding dwheel action etc, also, loosen up replay tolerances.improve replays that are cut in the middle of gui working on riding boats / replays cut in the middle of a run.The following is a list of the available versions:Ħ.x Core recorder features subject to change ⬇️ index fileĦ.9 First feature complete recorder versionĦ.10 Fixes mouse scaling on Mac when gui is openĦ.13 Sprinting, swap-hands. We do not collect any data that is unrelated to minecraft from your computer. 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. We are collecting data for training AI models in Minecraft. Version where the task changed significantly. View for each version, but we've shared the prompts and major clarifications for each recorder It is intractable to share every contractor's Result in a behavioral change in the other contractor. Also, as contractors internally ask questions, clarification from one contractor may Recorder versions we asked contractors to change their username when recording particular Prompt or recording feature while bug-fixes were represented as minor version changes. In general, major recorder versions change for a new Over the course of the project we requested various demonstrations from contractors Computes gradients one sample at a time to keep memory use low, but also slows down the code.Only trains single step at the time, i.e., errors are not propagated through timesteps.You can change the training settings at the top of behavioural_cloning.py. You can then use finetuned-1x.weights when running the agent. Python behavioural_cloning.py -data-dir data -in-model foundation-model-1x.model -in-weights foundation-model-1x.weights -out-weights finetuned-1x.weights If you downloaded the "1x Width" models and placed some data under data directory, you can perform finetuning with With default settings, you need at least 12 recordings. jsonl files to the same directory (e.g., data). Download contractor data (below) and place the.model file for model you want to fine-tune. This code has been designed to be run-able on consumer hardware (e.g., 8GB of VRAM). Using behavioural cloning to fine-tune the modelsĭisclaimer: 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. Note that run_inverse_dynamics_model.py is designed to be a demo of the IDM, not code to put it into practice. Python 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.jsonlĪ window should pop up which shows the video frame-by-frame, showing the predicted and true (recorded) actions side-by-side on the left. To run the model with above files placed in the root directory of this code: For this demo we useĪnd this associated actions file (.jsonl). For demonstration purposes, you can use the contractor recordings shared below to.Install requirements: pip install -r requirements.txt.IDM aims to predict what actions player is taking in a video recording. On how they were trained and the exact reward schedule. Using a reward function and excel at progressing through the tech tree quickly. While less general then the behavioralĬloning models, these models have the benefit of interacting with the environment These models further refine the above demonstration based models with a rewardįunction targeted at obtaining diamond pickaxes. See the paper linked above for more details. Model further using either the housebuilding contractor data or early game video While house and early game models refine their respective size foundational Use reinforcement learning (RL) to further optimize the policy.įoundational models are trained across all videos in a single training run Using behavioral cloning (BC) and are more general than later models which These models are trained on video demonstrations of humans playing Minecraft The 1x, 2x and 3x model files correspond to their respective model weights width. Agent Model Zooīelow are the model files and weights files for various pre-trained Minecraft models. Python run_agent.py -model -weights Īfter loading up, you should see a window of the agent playing Minecraft.
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