Deep Q Network
Implementing Mnih et al. 2015
Alexandre Beaulne ID 260234941
Amina Madzhun ID 260844950
Apr 26 2018
Introduction
"Human-level control through deep reinforcement learning", Mnih et al., Nature 2015
Get RL agent to play Atari games with no knowledge beside screen action and rewards
Motivation
Learn about applying RL to more complex problems from an engineering perspective
Learn how deep learning and RL tie up in practice
Results
Before training:
After training:
Results
Before training
After training
Results
Conclusion
Deep RL is hard!
Debugging is difficult
Convergence of learning very sensitive to hyperparameters (notably ε)
Even when good hyperparameters are found, training takes a lot of time
Future Work
Learn what to look for when training: how to debug and measure progress
How to craft more sensible action space
Use ConvNet with more capacity
Use RNN (e.g. LSTM) instead of stacked frames to learn causation