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README.md

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 license: mit
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-[NOTICE] This dataset is currently in transition from the OneDrive previously hosted here:  
-https://github.com/TeaPearce/Counter-Strike_Behavioural_Cloning?tab=readme-ov-file#datasets  
+This repo hosts the dataset presented in:  
+__Counter-Strike Deathmatch with Large-Scale Behavioural Cloning__  
+[Tim Pearce](https://teapearce.github.io/), [Jun Zhu](https://ml.cs.tsinghua.edu.cn/~jun/index.shtml)  
+IEEE Conference on Games (CoG) 2022 [⭐️ Best Paper Award!]  
+ArXiv paper: https://arxiv.org/abs/2104.04258 (Contains some extra experiments not in CoG version)  
+CoG paper: https://ieee-cog.org/2022/assets/papers/paper_45.pdf  
+Four minute introduction video: https://youtu.be/rnz3lmfSHv0  
+Gameplay examples: https://youtu.be/KTY7UhjIMm4  
+Code: https://github.com/TeaPearce/Counter-Strike_Behavioural_Cloning  
 
 
-Dataset presented in:  
-Counter-Strike Deathmatch with Large-Scale Behavioural Cloning, IEEE Conference on Games (CoG) 2022 [Best Paper Award]  
-https://arxiv.org/abs/2104.04258  
-Tim Pearce, Jun Zhu  
+The dataset comprises several different subsets of data as described below.  
+You probably only care about the first one (if you want the largest dataset), or the second or third one (if you care about clean expert data).
 
-Other works used in:  
-Imitating Human Behaviour with Diffusion Models, ICLR 2023  
+- ```hdf5_dm_july2021_*_to_*.tar```
+    - each .tar file contains 200 .hdf5 files
+    - total files when unzipped: 5500
+    - approx size: 700 GB
+    - map: dust2
+    - gamemode: deathmatch
+    - source: scraped from online servers
+
+- ```dataset_dm_expert_dust2/hdf5_dm_july2021_expert_*.hdf5```
+    - total files when unzipped: 190
+    - approx size: 24 GB
+    - map: dust2
+    - gamemode: deathmatch
+    - source: manually created, clean actions
+
+- ```dataset_aim_expert/hdf5_aim_july2021_expert_*.hdf5```
+    - total files when unzipped: 45
+    - approx size: 6 GB
+    - map: aim map
+    - gamemode: aim mode
+    - source: manually created, clean actions
+
+- ```dataset_dm_expert_othermaps/hdf5_dm_nuke_expert_*.hdf5```
+    - total files when unzipped: 10
+    - approx size: 1 GB
+    - map: nuke
+    - gamemode: deathmatch
+    - source: manually created, clean actions
+
+- ```dataset_dm_expert_othermaps/hdf5_dm_mirage_expert_*.hdf5```
+    - total files when unzipped: 10
+    - approx size: 1 GB
+    - map: mirage
+    - gamemode: deathmatch
+    - source: manually created, clean actions
+
+- ```dataset_dm_expert_othermaps/hdf5_dm_inferno_expert_*.hdf5```
+    - total files when unzipped: 10
+    - approx size: 1 GB
+    - map: mirage
+    - gamemode: deathmatch
+    - source: manually created, clean actions
+
+- ```dataset_metadata/currvarsv2_dm_july2021_*_to_*.npy, currvarsv2_dm_july2021_expert_*_to_*.npy, currvarsv2_dm_mirage_expert_1_to_100.npy, currvarsv2_dm_inferno_expert_1_to_100.npy, currvarsv2_dm_nuke_expert_1_to_100.npy, currvarsv2_aim_july2021_expert_1_to_100.npy```
+    - total files when unzipped: 55 + 2 + 1 + 1 + 1 + 1 = 61
+    - approx size: 6 GB
+    - map: as per filename
+    - gamemode: as per filename
+    - source: as per filename
+
+- ```location_trackings_backup/```
+    - total files when unzipped: 305
+    - approx size: 0.5 GB
+    - map: dust2
+    - gamemode: deathmatch
+    - source: contains metadata used to compute map coverage analysis
+        - **currvarsv2_agentj22** is the agent trained over the full online dataset
+        - **currvarsv2_agentj22_dmexpert20** is previous model finetuned on the clean expert dust2 dataset
+        - **currvarsv2_bot_capture** is medium difficulty built-in bot
+
+
+
+### Structure of .hdf5 files (image and action labels -- you probably care about this one): 
+
+Each file contains an ordered sequence of 1000 frames (~1 minute) of play. 
+This contains screenshots, as well as processed action labels.
+We chose .hdf5 format for fast dataloading, since a subset of frames can be accessed without opening the full file.
+The lookup keys are as follows (where i is frame number 0-999)
+- **frame_i_x**: is the image 
+- **frame_i_xaux**: contains actions applied in previous timesteps, as well as health, ammo, and team. see dm_pretrain_preprocess.py for details, note this was not used in our final version of the agent
+- **frame_i_y**: contains target actions in flattened vector form; [keys_pressed_onehot, Lclicks_onehot, Rclicks_onehot, mouse_x_onehot, mouse_y_onehot]
+- **frame_i_helperarr**: in format [kill_flag, death_flag], each a binary variable, e.g. [1,0] means the player scored a kill and did not die in that timestep
+
+### Structure of .npy files (scraped metadata -- you probably don't care about this): 
+
+Each .npy file contains metadata corresponding to 100 .hdf5 files (as indicated by file name)
+They are dictionaries with keys of format: file_numi_frame_j for file number i, and frame number j in 0-999
+The values are of format **[curr_vars, infer_a, frame_i_helperarr]** where, 
+- **curr_vars**: contains a dictionary of the metadata originally scraped -- see dm_record_data.py for details
+- **infer_a**: are inferred actions, [keys_pressed,mouse_x,mouse_y,press_mouse_l,press_mouse_r], with mouse_x and y being continuous values and keys_pressed is in string format
+- **frame_i_helperarr**: is a repeat of the .hdf5 file
+
+
+## Trained Models
+
+Four trained models are provided. There are 'non-stateful' (use during training) and 'stateful' (use at test time) versions of each.  
+Models can be downloaded under ```trained_models.zip```.
+
+- ```ak47_sub_55k_drop_d4```
+    : Pretrained on AK47 sequences only.
+- ```ak47_sub_55k_drop_d4_dmexpert_28```
+    : Finetuned on expert deathmatch data.
+- ```ak47_sub_55k_drop_d4_aimexpertv2_60```
+    : Finetuned on aim mode expert data.
+- ```July_remoterun7_g9_4k_n32_recipe_ton96__e14```
+    : Pretrained on full dataset.
+
+
+
+
+## Other works using the dataset:
+
+- __Imitating Human Behaviour with Diffusion Models, ICLR 2023__  
 https://arxiv.org/abs/2301.10677  
 Tim Pearce, Tabish Rashid, Anssi Kanervisto, Dave Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, Sam Devlin  
 
-Diffusion for World Modeling: Visual Details Matter in Atari, NeurIPS 2024  
+- __Diffusion for World Modeling: Visual Details Matter in Atari, NeurIPS 2024__  
 https://arxiv.org/pdf/2405.12399  
 Eloi Alonso∗, Adam Jelley∗, Vincent Micheli, Anssi Kanervisto, Amos Storkey, Tim Pearce‡, François Fleuret‡  
 Tweet here: https://twitter.com/EloiAlonso1/status/1844803606064611771