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Teaching a Machine to Read Maps with Deep Reinforcement Learning
Gino Brunner, Oliver Richter, Yuyi Wang, Roger Wattenhofer ETH Zurich
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Our map Our view Deepmind randomly generated, starting on small maps
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Reinforcement Learning
Environment Reward State Action Finding the target Bumping into walls Compass Agent
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What does it mean to read a map?
Localize
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What does it mean to read a map?
Localize Find a path to the target Follow the path I think I’m here
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t + 1 Visual Input I think I’m here Estimated Position Map Input
Compass Visible Local Map Network Recurrent Localization Cell Estimated Position I think I’m here Map Input Policy π Location Uncertainty Acting Agent Map Interpretation Network Short Term Target Direction
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Modular design as key to success
Visible Local Map Network Map Interpretation Network Acting Agent Recurrent Localization Cell Estimated Position Reward Prediction Policy π Actual Position Actual Reward Reinforcement Learning Neural nets, heuristics, explicit algorithms
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Results
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Questions? Oliver Richter richtero@ethz.ch
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We and robots navigate with maps and gps
Or SLAM >no sensors but map> human
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Visible Local Map Network
FC Orientation Visual Input Visible Local Map FC Visible Field Map Excerpt CNN FC
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