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Migrating-v0.3.md

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Migrating to ML-Agents v0.3

There are a large number of new features and improvements in ML-Agents v0.3 which change both the training process and Unity API in ways which will cause incompatibilities with environments made using older versions. This page is designed to highlight those changes for users familiar with v0.1 or v0.2 in order to ensure a smooth transition.

Important

  • ML-Agents is no longer compatible with Python 2.

Python Training

  • The training script ppo.py and PPO.ipynb Python notebook have been replaced with a single learn.py script as the launching point for training with ML-Agents. For more information on using learn.py, see here.
  • Hyperparameters for training brains are now stored in the trainer_config.yaml file. For more information on using this file, see here.

Unity API

  • Modifications to an Agent's rewards must now be done using either AddReward() or SetReward().
  • Setting an Agent to done now requires the use of the Done() method.
  • CollectStates() has been replaced by CollectObservations(), which now no longer returns a list of floats.
  • To collect observations, call AddVectorObs() within CollectObservations(). Note that you can call AddVectorObs() with floats, integers, lists and arrays of floats, Vector3 and Quaternions.
  • AgentStep() has been replaced by AgentAction().
  • WaitTime() has been removed.
  • The Frame Skip field of the Academy is replaced by the Agent's Decision Frequency field, enabling agent to make decisions at different frequencies.
  • The names of the inputs in the Internal Brain have been changed. You must replace state with vector_observation and observation with visual_observation. In addition, you must remove the epsilon placeholder.

Semantics

In order to more closely align with the terminology used in the Reinforcement Learning field, and to be more descriptive, we have changed the names of some of the concepts used in ML-Agents. The changes are highlighted in the table below.

Old - v0.2 and earlier New - v0.3 and later
State Vector Observation
Observation Visual Observation
Action Vector Action
N/A Text Observation
N/A Text Action