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com.unity.ml-agents_1.0.0
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latest_release
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v0.1
ml-agents/ml-agents
GitHub
1955af9e
[feature] Add experimental PyTorch support (#4335)
* Begin porting work * Add ResNet and distributions * Dynamically construct actor and critic * Initial optimizer port * Refactoring policy and optimizer * Resolving a few bugs * Share more code between tf and torch policies * Slightly closer to running model * Training runs, but doesn’t actually work * Fix a couple additional bugs * Add conditional sigma for distribution * Fix normalization * Support discrete actions as well * Continuous and discrete now train * Mulkti-discrete now working * Visual observations now train as well * GRU in-progress and dynamic cnns * Fix for memories * Remove unused arg * Combine actor and critic classes. Initial export. * Support tf and pytorch alongside one another * Prepare model for onnx export * Use LSTM and fix a few merge errors * Fix bug in probs calculation * Optimize np -> tensor operations * Time action sample funct... |
4 年前 | |
|---|---|---|
| .. | ||
| mlagents | [feature] Add experimental PyTorch support (#4335) | 4 年前 |
| tests | Update barracuda in the hopes that our burst crashes go away. (#4359) (#4365) | 4 年前 |
| README.md | [refactor] Store and restore state along with checkpoints (#4025) | 5 年前 |
| setup.py | Update add-fire to latest master, including Policy refactor (#4263) | 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.