Question Self-Play: Agent manager was not created for behavior id Soccer?team=1

I am trying to setup up an environment where two agents use self-play to train.

This is a turn-based soccer game where an agent can move one of its players a chosen distance and direction each turn. I have two agents, each of which have control over all of their team's players.

When running mlagents-learn I get the following logs

• INFO [environment.py:265] Connected new brain: Soccer?team=2
• INFO [stats.py:130] Hyperparameters for behavior name Soccer:
  trainer_type: ppo
  hyperparameters:
  batch_size: 2048
  buffer_size: 20480
  learning_rate: 0.0003
  beta: 0.005
  epsilon: 0.2
  lambd: 0.95
  num_epoch: 3
  learning_rate_schedule: constant
  network_settings:
  normalize: False
  hidden_units: 64
  num_layers: 2
  vis_encode_type: simple
  memory: None
  reward_signals:
  extrinsic:
  gamma: 0.99
  strength: 1.0
  init_path: None
  output_path: results\test\Soccer
  keep_checkpoints: 5
  max_steps: 50000000
  time_horizon: 1000
  summary_freq: 10000
  threaded: True
  self_play:
  save_steps: 50000
  team_change: 200000
  swap_steps: 50000
  window: 10
  play_against_latest_model_ratio: 0.5
  initial_elo: 1200.0
  behavioral_cloning: None
  
  
• INFO [environment.py:265] Connected new brain: Soccer?team=1
• WARNING [env_manager.py:109] Agent manager was not created for behavior id Soccer?team=1.

I am not sure what is causing the Agent manager warning. When running the SoccerTwos example, I do not get this warning, but I am having a hard time figuring out the difference between my environment and the SoccerTwos environment.

Version information:
ml-agents: 0.17.0.dev0,
ml-agents-envs: 0.17.0.dev0,
Communicator API: 1.0.0,
TensorFlow: 2.2.0

My Unity Version is 2019.1.0f2.

Has anyone else run into this issue or have any ideas how to solve it?
I've seen this issue on the ml-agents GitHub, but there doesn't seem to be a resolution yet.
Any help would be appreciated!

 

(I am the person who filed the github issue). Perhaps you can comment on the issue and say that you have the same problem?

 

Sure. Are you saying you can't reproduce this though?

 

Oh sorry. I misunderstood who was asking. I can't use the example environments because there are no such environments that use manual stepping. I posted the package in the original bug, here it is also: https://drive.google.com/file/d/1akL4mAGvSU8MZNp8xU5INxLCaRQu1KBb/view?usp=sharing