Unity Machine Learning Agents: Transforming Games with Artificial Intelligence

What are Unity Machine Learning Agents?

Traditionally, game developers have been hard-coding the behaviour of the game agents. Although effective, this is a tedious task and it also limits the intelligence of the agents. Simply put, the agents are not smart enough. To overcome this obstacle, Unity have simplified the training process for the game developers and researchers by introducing Unity Machine Learning Agents (ML-Agents, in short). Through just a simple Python API, the game agents can be now trained to use deep reinforcement learning, an advanced form of machine learning, to learn from their actions and modify their behaviour accordingly. These agents can then be used to dynamically modify the difficulty of the game.

How do they work?

As mentioned earlier, the Unity ML-Agents are designed to work based on the concept of deep reinforcement learning, a branch of machine learning where the agents are trained to learn from their own actions. Here is a simple flowchart to demonstrate how reinforcement learning works:

The reinforcement learning training cycle

The learning environment to be configured for the ML-agents consists of 3 primary objects:

• Agent: Every agent has a unique set of states, observations and actions within the environment, and is assigned rewards for particular events.
• Brain: A brain decides what action any agent is supposed to take in a particular scenario. Think of it as a regular human brain, which basically controls the bodily functions.
• Academy: This object contains all the brains within the environment
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To train the agents, a variety of scenarios are made possible by varying the connection of different components (explained above) of the environment. Some are single agents, some simultaneous single agents, and others could be co-operative and competitive multi-agents and more. You can read more about these possibilities on the official Unity blog.

Apart from the way these agents are trained, Unity are also adding some cool new features in these ML-agents. Some of these are:

• Monitoring the agents’ decision-making to make it more accurate
• Incorporating curriculum learning, by which the complexity of the tasks can eventually be increased to aid more effective learning
• Imitation learning is a newly-introduced feature wherein the agents simply mimic the actions we want them to perform, rather than they learning on their own.

What next for Unity Machine Learning Agents?

Unity recently announced the release of v0.3 beta SDK of the ML-agents, and have been making significant progress in this domain to develop smarter, more intelligent game agents which can be used with the Unity game engine. Still very much in the research phase, these agents can also be used as an example by academic researchers to study the complex behaviour of trained models in different environments and scenarios where the variables associated with the in-game physics and visual appearance can be altered. Going forward, these agents can also be used by enterprises for large scale simulations, in robotics and also in the development of autonomous vehicles.

These are interesting times for game developers, and Unity in particular, in their quest for developing smarter, cutting-edge games. Inclusion of machine learning in their game development strategy is a terrific move, although it will take some time for this to be perfected and incorporated seamlessly. Nonetheless, all the research and innovation being put into this direction certainly seems well worth it!