ml-agents/docs/Feature-Memory.md

Using Recurrent Neural Networks in ML-Agents

What are memories 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.

It is now possible to give memories to your agents. When training, the agents will be able to store a vector of floats to be used next time they need to make a decision.

Deciding what the agents should remember in order to solve a task is not easy to do by hand, but our training algorithms can learn to keep track of what is important to remember with LSTM.

How to use

When configuring the trainer parameters in the trainer_config.yaml file, add the following parameters to the Brain you want to use.

	use_recurrent: true
	sequence_length: 64
	memory_size: 256

• use_recurrent is a flag that notifies the trainer that you want to use a Recurrent Neural Network.
• sequence_length defines how long the sequences of experiences must be while training. In order to use a LSTM, training requires a sequence of experiences instead of single experiences.
• memory_size corresponds to the size of the memory the agent must keep. Note that if this number is too small, the agent will not be able to remember a lot of things. If this number is too large, the neural network will take longer to train.

Limitations

• LSTM does not work well with continuous vector action space. Please use discrete vector action space for better results.
• Since the memories must be sent back and forth between Python and Unity, using too large memory_size will slow down training.
• Adding a recurrent layer increases the complexity of the neural network, it is recommended to decrease num_layers when using recurrent.
• It is required that memory_size be divisible by 4.