We will first learn about TD learning. This is a very fundamental concept in RL. In TD learning, the learning of the agent is attained by experience. Several trial episodes are undertaken of the environment, and the rewards accrued are used to update the value functions. Specifically, the agent will keep an update of the state-action value functions as it experiences new states/actions. The Bellman equation is used to update this state-action value function, and the goal is to minimize the TD error. This essentially means the agent is reducing its uncertainty of which action is the optimal action in a given state; it gains confidence on the optimal action in a given state by lowering the TD error.
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You're reading from TensorFlow Reinforcement Learning Quick Start Guide
Kaushik Balakrishnan works for BMW in Silicon Valley, and applies reinforcement learning, machine learning, and computer vision to solve problems in autonomous driving. Previously, he also worked at Ford Motor Company and NASA Jet Propulsion Laboratory. His primary expertise is in machine learning, computer vision, and high-performance computing, and he has worked on several projects involving both research and industrial applications. He has also worked on numerical simulations of rocket landings on planetary surfaces, and for this he developed several high-fidelity models that run efficiently on supercomputers. He holds a PhD in aerospace engineering from the Georgia Institute of Technology in Atlanta, Georgia.
Read more about Kaushik Balakrishnan
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Kaushik Balakrishnan works for BMW in Silicon Valley, and applies reinforcement learning, machine learning, and computer vision to solve problems in autonomous driving. Previously, he also worked at Ford Motor Company and NASA Jet Propulsion Laboratory. His primary expertise is in machine learning, computer vision, and high-performance computing, and he has worked on several projects involving both research and industrial applications. He has also worked on numerical simulations of rocket landings on planetary surfaces, and for this he developed several high-fidelity models that run efficiently on supercomputers. He holds a PhD in aerospace engineering from the Georgia Institute of Technology in Atlanta, Georgia.
Read more about Kaushik Balakrishnan