ActiveRagdollStyleTransfer

Research into using mocap (and longer term video) as style reference for training Active Ragdolls / locomotion for Video Games

(using Unity ML_Agents + MarathonEnvs)

---

Goals

• Train active ragdolls using style reference from MoCap / Videos
• Integrate with ActiveRagdollAssaultCourse & ActiveRagdollControllers

---

Using this repro

• Make sure you are using a compatable version of Unity (tested with 2018.4 LTS and 2019.1)

• To run trained models, make sure you: add TensorFlowSharp to Unity

• To try different moves: Replace reference MoCap in animation tree and select the right ML-Agent trained model

• To re-train:

  • Set the LearnFromMocapBrain to External
  • Build the project
  • From the root path, invoke the python script like this: mlagents-learn config\style_transfer_config.yaml --train --env="\b\StyleTransfer002\Unity Environment.exe" --run-id=StyleTransfer002-145 where "\b\StyleTransfer002\Unity Environment.exe" points to the built project and StyleTransfer002-145 is the unique name for this run. (Note: use / if on MacOS/Linux)
  • See the ML-Agents documentation for more details on using ML-Agents

• Post an Issue if you are still stuck

---

Contributors

• Joe Booth (SohoJoe)

---

Download builds : Releases

---

StyleTransfer002

Backflip (002.144-128m steps)

Running (002.114)	Walking (002.113)

• Model: MarathonMan (modified MarathonEnv.DeepMindHumanoid)
• Animation: Runningv2, Walking, Backflip
• Hypostheis: Implement basic style transfer from mo-cap using MarathonEnv model
• Outcome: Is now training on Backflip
  • Initial was able to train walking but not running (16m steps / 3.2m observations)
  • Through tweaking model was able to train running (32m steps / 6.4m observations)
  • Was struggling to train backflip but looks like I need to train for longer (current example is 48m steps / 9.6m observations)
  • Was able to train Backflip after updating to Unity 2018.3 beta - looks like updates to PhyX engine improve stability
• References:
  • Insperation: DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills arXiv:1804.02717 [cs.GR]
• Notes:
  • Needed to make lots of modifications to model to improve training performance
  • Added sensors to feet improved trainging
  • Tweaking joints improved training
  • Training time was = ~7h for 16m steps (3.2m observations) TODO: check assumptions
  • New Training time is + 2x
  • ... Optimization: Hack to Academy to have 4 physics only steps per ml-step
  • ... Optimization: Train with 64 agents
  • ... also found training in headless mode --no-graphics helped
  • Updated to Unity 2018.3 Beta for PhysX improvements
  • see RawNotes.002 for details on each experiment

StyleTransfer001

• Model: U_Character_REFAvatar
• Animation: HumanoidWalk
• Hypostheis: Implement basic style transfer from mo-cap
• Outcome: FAIL
  • U_Character_REFAvatar + HumanoidWalk has an issue whereby the feet collide. The RL does get learn to avoid - but it feels that this is slowing it down
• References:
  • Insperation: [DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills arXiv:1804.02717 [cs.GR]](https://arxiv.org/abs/1804.02717
• Raw Notes:
  • Aug 27 2018: Migrate to new repro and tidy up code so to make open source