* added broadcast to the player and heuristic brain.
Allows the python API to record actions taken along with the states and rewards
* removed the broadcast checkbox
Added a Handshake method for the communicator
The academy will try to handshake regardless of the brains present
Player and Heuristic brains will send their information through the communicator but will not receive commands
* bug fix : The environment only requests actions from external brains when unique
* added warning in case no brins are set to external
* fix on the instanciation of coreBrains,
fix on the conversion of actions to arrays in the BrainInfo received from step
* default discrete action is now 0
bug fix for discrete broadcast action (the action size should be one in Agents.cs)
modified Tennis so that the default action is no action
modified the TemplateDecsion.cs to ensure non null values are sent from Decide() and MakeMemory()
* minor fixes
* need to convert the s...
* `learn.py` is now main script for training brains.
* Simultaneous multi-brain training is now possible.
* `ghost-trainer` allows for proper training in adversarial scenarios.
* `imitation-trainer` provides a basic implementation of real-time behavioral cloning.
* All trainer hyperparameters now exist in `.yaml` files.
* `PPO.ipynb` removed.
* LSTM model added.
* More dynamic buffer class to handle greater variety of scenarios.
* Check that worker port is available in RpcCommunicator
Previously the RpcCommunicator did not check the port or create the
RPC server until `initialize()` was called. Since "initialize"
requires the environment to be available, this means we might create
a new environment which connects to an existing RPC server running
in another process. This causes both training runs to fail.
As a remedy to this issue, this commit moves the server creation into
the RpcCommunicator constructor and adds an explicit socket binding
check to the requested port.
* Fixes suggested by Codacy
* Update rpc_communicator.py
* Addressing feedback: formatting & consistency
* Add Sampler and SamplerManager
* Enable resampling of reset parameters during training
* Documentation for Sampler and example YAML configuration file
This fixes an issue where stopping the game when training in the Editor won't end training, due to the new asynchronous SubprocessEnvManager changes. Another minor change was made to move the `env_manager.close()` in TrainerController to the end of `start_learning` so that we are more likely to save the model if something goes wrong during the environment shutdown (this occurs sometimes on Windows machines).
* pass shape to WriteAdapter
* handle floats on python side
* cleanup
* whitespace
* rename GetFloatObservationShape, support uncompressed in RenderTexture sensor
* numpy float32
* remove unused using
* Float sensor and unit test
* replace asserts with exceptions, docstrings
* Make ChannelId a property and renamed ReservedChannelId
* Changes on the Python side for consistency
* Modified the tutorial appropriately
* fixing bugs
* Update ml-agents-envs/mlagents_envs/environment.py
Co-Authored-By: Chris Elion <chris.elion@unity3d.com>
* Update com.unity.ml-agents/Runtime/Grpc/RpcCommunicator.cs
Co-Authored-By: Chris Elion <chris.elion@unity3d.com>
* Addressing comments
* Update docs/Python-API.md
Co-Authored-By: Chris Elion <chris.elion@unity3d.com>
* Added a Utils class on the side channel (#3447)
- No change in user facing API
- Simplifies the code in the side channel implementations as it makes it easier to check if a side channel id is within ranges
- No changes to tests
- No changes to Documentation
* Simplifying
* Fixing a bug
* Replace the int ChannelId with a GUID/UUID ChannelId (#3454)
* renaming channel_type to channel_id
* Making the constant GUID const...
Arthur Juliani
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Deric Pang
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Chris Elion
Ervin T
Jonathan Harper
sankalp04
Andrew Cohen
Anupam Bhatnagar
Christopher Goy
Ruo-Ping Dong