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com.unity.ml-agents_1.0.0
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v0.1
ml-agents/ml-agents
GitHub
d36a5242
Python Dataflow for Group Manager (#4926)
* Make buffer type-agnostic * Edit types of Apped method * Change comment * Collaborative walljump * Make collab env harder * Add group ID * Add collab obs to trajectory * Fix bug; add critic_obs to buffer * Set group ids for some envs * Pretty broken * Less broken PPO * Update SAC, fix PPO batching * Fix SAC interrupted condition and typing * Fix SAC interrupted again * Remove erroneous file * Fix multiple obs * Update curiosity reward provider * Update GAIL and BC * Multi-input network * Some minor tweaks but still broken * Get next critic observations into value estimate * Temporarily disable exporting * Use Vince's ONNX export code * Cleanup * Add walljump collab YAML * Lower max height * Update prefab * Update prefab * Collaborative Hallway * Set num teammates to 2 * Add config and group ids to HallwayCollab * Fix bug with hallway collab * E... |
4 年前 | |
|---|---|---|
| .. | ||
| mlagents | Python Dataflow for Group Manager (#4926) | 4 年前 |
| tests | WebGL compile test (#4966) | 4 年前 |
| README.md | [refactor] Store and restore state along with checkpoints (#4025) | 5 年前 |
| setup.py | master -> main. (#5010) (#5044) | 4 年前 |
README.md
Unity ML-Agents Trainers
The mlagents Python package is part of the
ML-Agents Toolkit. mlagents
provides a set of reinforcement and imitation learning algorithms designed to be
used with Unity environments. The algorithms interface with the Python API
provided by the mlagents_envs package. See here for
more information on mlagents_envs.
The algorithms can be accessed using the: mlagents-learn access point. See
here for more information on using this
package.
Installation
Install the mlagents package with:
pip3 install mlagents
Usage & More Information
For more information on the ML-Agents Toolkit and how to instrument a Unity scene with the ML-Agents SDK, check out the main ML-Agents Toolkit documentation.
Limitations
mlagentsdoes not yet explicitly support multi-agent scenarios so training cooperative behavior among different agents is not stable.- Resuming self-play from a checkpoint resets the reported ELO to the default value.