A visual depiction of how an Agents Learning Environment might be configured within ML-Agents.
A visual depiction of how an Learning Environment might be configured within ML-Agents.
The three main kinds of objects within any Agents Learning Environment are:
* **Complex Visual Observations** - Unlike other platforms, where the agent’s observation might be limited to a single vector or image, ML-Agents allows multiple cameras to be used for observations per agent. This enables agents to learn to integrate information from multiple visual streams, as would be the case when training a self-driving car which required multiple cameras with different viewpoints, a navigational agent which might need to integrate aerial and first-person visuals, or an agent which takes both a raw visual input, as well as a depth-map or object-segmented image.
* **Imitation Learning (Coming Soon)** - It is often more intuitive to simply demonstrate the behavior we want an agent to perform, rather than attempting to have it learn via trial-and-error methods. In a future release, ML-Agents will provide the ability to record all state/action/reward information for use in supervised learning scenarios, such as imitation learning. By utilizing imitation learning, a player can provide demonstrations of how an agent should behave in an environment, and then utilize those demonstrations to train an agent in either a standalone fashion, or as a first-step in a reinforcement learning process.
* **Imitation Learning (Coming Soon)** - It is often more intuitive to simply demonstrate the behavior we want an agent to perform, rather than attempting to have it learn via trial-and-error methods. In a future release, ML-Agents will provide the ability to record all state/action/reward information for use in supervised learning scenarios, such as imitation learning. By utilizing imitation learning, a player can provide demonstrations of how an agent should behave in an environment, and then utilize those demonstrations to train an agent in either a standalone fashion, or as a first-step in a reinforcement learning process.
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