The ML-Agents Toolkit allows researchers and game developers to build and train agents in Unity environments using Reinforcement Learning (RL). RL is useful when it is easier to specify what task an agent needs to complete rather than how to do it. An agent learns to select actions to complete a task on its own using observations from its environment and a task reward signal. Here’s how our interns improved our RL and ML-agents this summer.

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