• lecture slides I, slides II

• Our lecture, second lecture, seminar (russian)

• David Silver lecture - video

• More practical and less theoretical lecture from MIT 6.S191 - video

• Understanding approximate q-learning - url

• Karpathy's post on approximate RL - url

• [recommended] How to actually do deep reinforcement learning by J. Schulman - pdf
• [recommended] An overview of deep reinforcement learning - arxiv
• DQN and modiffications - lecture by J. Schulman - video
• • interactive demos in your browser: demo1(karpathy), demo2(Hünermann)
• Reinforcement learning architectures list - repo
• Article on dueling DQN - arxiv
• Article on double DQN - arxiv
• Article on prioritized experience replay - arxiv
• Article on bootstrap DQN - pdf, summary
• Article on asynchronuous methods in deep RL - arxiv
• Successor representations for reinforcement learning - article, video
• Video on asynchronuous methods (Mnih) - video

• [in pytorch] A great series starting from simple DQN to all the cool new stuff - url
• A guide to deep RL from ~scratch (nervana blog) - url
• Building deep q-network from ~scratch (blog) - url
• Another guide guide to DQN from ~scratch (blog) - url

From now on, we have two tracks, theano and tensorflow. We'll also add pytorch support soon.

You can choose whichever track you want, but unless you're expertly familiar with your framework, we recommend you to start by completing the task in lasagne and only then reproduce your solution in your chosen framework.

Begin with seminar_<framework>.ipynb and then proceed with homework_<framework>.ipynb.

__Note: you're not required to submit assignments in all three frameworks. Pick one and go with it. Maybe switch it occasionally if you want more challenge. __