How severe does this issue affect your experience of using Ray?
- High: It blocks me to complete my task.
Heya, I have a custom environment built in Unity. In Unity, I use ML-Agents, which also provides an
Academy.Instance.StatsRecorder to track custom data in the environment. This is how I was hoping this would work:
from mlagents_envs.side_channel.environment_parameters_channel import EnvironmentParametersChannel from mlagents_envs.side_channel.stats_side_channel import StatsSideChannel env_setup_channel = EnvironmentParametersChannel() env_setup_channel .set_float_parameter("training", 1) # This is what I'm interested in: stats_channel = StatsSideChannel() tune.register_env( "unity3d", lambda c: CustomEnv( file_name=FILE_NAME, no_graphics=True, episode_horizon=EPISODE_HORIZON, side_channel=[env_setup_channel, stats_channel], ), )
env_setup_channel is passing parameters to the Unity environment, which works well when I use RLLibs
tune.Tuner to run training:
results = tune.Tuner( "PPO", param_space=config.to_dict(), run_config=air.RunConfig( stop=stop, verbose=2, checkpoint_config=air.CheckpointConfig( checkpoint_frequency=5, checkpoint_at_end=True, ), ), ).fit()
However I was expecting for the stats that I’m adding to my
Academy.Instance.StatsRecorder in the Unity environment, would show up when I open the training results in tensorboard. (this is the behaviour in Unitys ML-Agents)
Am I missing something? Maybe sven1977 has an idea? Thank you!