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Episode Info: Introduction to reinforcement learning concepts ## Resources - Hands-On Machine Learning with Scikit-Learn and TensorFlow (http://amzn.to/2tVdIXN) `book:medium` (last chapter) - Sutton & Barto 2nd Ed PDF (http://incompleteideas.net/book/the-book-2nd.html) `book:hard` - AI a Modern Approach. Website (http://aima.cs.berkeley.edu/), Book (http://amzn.to/2E02dEr) `book:hard` - Berkeley cs294: Deep Reinforcement Learning (http://rll.berkeley.edu/deeprlcourse/) `course:hard` - RL Course by David Silver (https://www.youtube.com/playlist?list=PLzuuYNsE1EZAXYR4FJ75jcJseBmo4KQ9-) `course|audio:hard` - Convert video to audio: ** mp4 => mp3: `for f in *.mp4; do ffmpeg -i "$f" "${f%.mp4}.mp3" && rm "$f"; done` ** youtube => mp3: setup youtube-dl (https://github.com/rg3/youtube-dl) and run `youtube-dl -x youtube.com/playlist?list=` ## Episode - RL definition: goal, rewards, actions ** Games (Atari, Chess, Go - Lee Sedol & Alpha Go) ** AI: learning, vision / speech, action / motion, planning ** Reasoning / knowledge vs model-based Deep RL? ** Reasoning / knowledge rep (+memory?) => Differential computers (https://deepmind.com/blog/differentiable-neural-computers/) ** vs supervised. Vision = supervised. Games = action. Trading can go both ways! ** Time: Credit assignment, delayed rewards, investment - Model-based v free ** Policy (what you do; gut reaction) - Value-based (Q-learning) vs Policy Gradient ** PG is direct: ML -> action ** Value-based indirect: Bellman stuff -> state/action values (Q-values) -> policy - Openai Gym, cartpole - Frameworks ** openai/baselines (https://github.com/openai/baselines) ** reinforceio/tensorforce (https://github.com/reinforceio/tensorforce) ** NervanaSystems/coach (https://github.com/NervanaSystems/coach) ** rll/rllab (https://github.com/rll/rllab)Read more ยป

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