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last bit of doc review from checklist (#1191)

* minor grammatical fix

* fixed definition in table
/develop-generalizationTraining-TrainerController
GitHub 6 年前
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共有 2 个文件被更改,包括 4 次插入3 次删除
  1. 2
      docs/Feature-Memory.md
  2. 5
      docs/Training-ML-Agents.md

2
docs/Feature-Memory.md


# Memory-enhanced agents using Recurrent Neural Networks
## What are memories for
## What are memories used for?
Have you ever entered a room to get something and immediately forgot what you
were looking for? Don't let that happen to your agents.

5
docs/Training-ML-Agents.md


settings. (This GameObject will be a child of the Academy in your scene.)
Sections for the example environments are included in the provided config file.
| **Setting** | **Description** | **Applies To Trainer**|
| **Setting** | **Description** | **Applies To Trainer\***|
| :-- | :-- | :-- |
| batch_size | The number of experiences in each iteration of gradient descent.| PPO, BC |
| batches_per_epoch | In imitation learning, the number of batches of training examples to collect before training the model.| BC |

| trainer | The type of training to perform: "ppo" or "imitation".| PPO, BC |
| use_curiosity | Train using an additional intrinsic reward signal generated from Intrinsic Curiosity Module. | PPO |
| use_recurrent | Train using a recurrent neural network. See [Using Recurrent Neural Networks](Feature-Memory.md).| PPO, BC |
|| PPO = Proximal Policy Optimization, BC = Behavioral Cloning (Imitation)) ||
\*PPO = Proximal Policy Optimization, BC = Behavioral Cloning (Imitation)
For specific advice on setting hyperparameters based on the type of training you
are conducting, see:

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