ML-Agents: Enabling Learned Behaviors with Reinforcement Learning

Description
Speaker
Behaviors in games---and in the real world---are often difficult to program explicitly. Reinforcement learning (RL) has shown success in learning behaviors based on a simple defined reward function that incentivises correct behavior.
Unity ML-Agents toolkit enables Unity developers to train reinforcement learning models to control behaviors within their games. Once these models are trained, they can be integrated across platforms into a game build via the Unity Inference Engine.
Furthermore, by enabling communication between a Unity build and Python code, ML-Agents enables RL researchers to use Unity games as training environments.
Ervin Teng

Research Engineer, Unity Technologies
  • Date: Jun 19, 10:00 (US Pacific Time)
  • Fee: Free
  • Available Seats: 3
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