- Is A3C an on-policy or off-policy algorithm?
- Why is the Shannon entropy term used?
- What are the problems with using a large number of worker threads?
- Why is softmax used in the policy neural network?
- Why do we need an advantage function?
- This is left as an exercise: For the LunarLander problem, repeat the training without reward shaping and see if the agent learns faster/slower than what we saw in this chapter.
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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