One of the main feedback we get is to illustrate more real game examples using ML-Agents. We are excited to provide an
example implementation of Match-3 using ML-Agents and additional utilities to integrate ML-Agents with Match-3 games.
Our aim is to enable Match-3 teams to leverage ML-Agents to create player agents to learn and play different Match-3
levels. This implementation is intended as a starting point and guide for teams to get started (as there are many
nuances with Match-3 for training ML-Agents) and for us to iterate both on the C#, hyperparameters, and trainers to
improve ML-Agents for Match-3.
This implementation includes:
* C# implementation catered toward a Match-3 setup including concepts around encoding for moves based on
[Human Like Playtesting with Deep Learning](https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning)
* An example Match-3 scene with ML-Agents implemented (located under /Project/Assets/ML-Agents/Examples/Match3).
More information on the Match-3 example is [here](https://github.com/Unity-Technologies/ml-agents/tree/release_15_docs/docs/docs/Learning-Environment-Examples.md#match-3).
### Feedback
If you are a Match-3 developer and are trying to leverage ML-Agents for this scenario,
[we want to hear from you](https://forms.gle/TBsB9jc8WshgzViU9). Additionally, we are also looking for interested
Match-3 teams to speak with us for 45 minutes. If you are interested, please indicate that in the
[form](https://forms.gle/TBsB9jc8WshgzViU9). If selected, we will provide gift cards as a token of appreciation.
### Interested in more game templates?
Do you have a type of game you are interested for ML-Agents? If so, please post a
[forum issue](https://forum.unity.com/forums/ml-agents.453/) with [GAME TEMPLATE] in the title.
The C# code for Match-3 exists inside of the extensions package (com.unity.ml-agents.extensions). A good first step
The C# code for Match-3 exists inside of the extensions package (com.unity.ml-agents.extensions). A good first step
under /Project/Assets/ML-Agents/Examples/match3). Once you have some familiarity, then the next step would be to
implement the C# code for Match-3 from the extensions package.
under /Project/Assets/ML-Agents/Examples/match3). Once you have some familiarity, then the next step would be to
implement the C# code for Match-3 from the extensions package.
Additionally, see below for additional technical specifications on the C# code for Match-3. Please note the Match-3
game isn't human playable as implemented and can be only played via training.
Additionally, see below for additional technical specifications on the C# code for Match-3. Please note the Match-3 game
isn't human playable as implemented and can be only played via training.
## Technical specifications for Match-3 with ML-Agents
[Human Like Playtesting with Deep Learning](https://www.researchgate.net/publication/328307928_Human-Like_Playtesting_with_Deep_Learning)
(for example, Figure 2b). The horizontal moves are enumerated first, then the vertical ones.
<imgsrc="images/match3-moves.png"align="center"/>
## Feedback
If you are a Match-3 developer and are trying to leverage ML-Agents for this scenario,
[we want to hear from you](https://forms.gle/TBsB9jc8WshgzViU9). Additionally, we are also looking for interested
Match-3 teams to speak with us for 45 minutes. If you are interested, please indicate that in the
[form](https://forms.gle/TBsB9jc8WshgzViU9). If selected, we will provide gift cards as a token of appreciation.
### Interested in more game templates?
Do you have a type of game you are interested for ML-Agents? If so, please post a
[forum issue](https://forum.unity.com/forums/ml-agents.453/) with [GAME TEMPLATE] in the title.
- The official version of Unity ML-Agents supports is now 2019.4 LTS. If you run
into issues, please consider deleting your project's Library folder and reponening your
project.
- If you used any of the APIs that were deprecated before version 2.0, you need to use their replacement. These deprecated APIs have been removed. See the migration steps bellow for specific API replacements.
- If you used any of the APIs that were deprecated before version 2.0, you need to use their replacement. These
deprecated APIs have been removed. See the migration steps bellow for specific API replacements.
### IDiscreteActionMask changes
- The interface for disabling specific discrete actions has changed. `IDiscreteActionMask.WriteMask()` was removed,
and replaced with `SetActionEnabled()`. Instead of returning an IEnumerable with indices to disable, you can
- The abstract method `SensorComponent.GetObservationShape()` was removed.
- The abstract method `SensorComponent.CreateSensor()` was replaced with `CreateSensors()`, which returns an `ISensor[]`.
### Match3 integration changes
The `AbstractBoard` interface was changed:
* `AbstractBoard` no longer contains `Rows`, `Columns`, `NumCellTypes`, and `NumSpecialTypes` fields.
* `public abstract BoardSize GetMaxBoardSize()` was added as an abstract method. `BoardSize` is a new struct that
contains `Rows`, `Columns`, `NumCellTypes`, and `NumSpecialTypes` fields, with the same meanings as the old
`AbstractBoard` fields.
* `public virtual BoardSize GetCurrentBoardSize()` is an optional method; by default it returns `GetMaxBoardSize()`. If
you wish to use a single behavior to work with multiple board sizes, override `GetCurrentBoardSize()` to return the
current `BoardSize`. The values returned by `GetCurrentBoardSize()` must be less than or equal to the corresponding
values from `GetMaxBoardSize()`.
## Migrating to Release 13
### Implementing IHeuristic in your IActuator implementations
GitHub
3 年前