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Control & Reinforcement Learning
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Interpretable Machine Learning via Program Synthesis - IPAM at UCLA
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- 1 Intro
- 2 What is Interpretability?
- 3 RNA Splicing Mechanism
- 4 RNA Splice Prediction
- 5 Control: Parallel Parking
- 6 Learning Interpretable Models
- 7 Program Synthesis for Interpretable ML
- 8 Video Trajectory Queries
- 9 Control & Reinforcement Learning
- 10 Deep Reinforcement Learning
- 11 Imitation Learning
- 12 Dataset Aggregation (DAgger)
- 13 Our Approach: Leverage the Q-Function
- 14 Viper Algorithm
- 15 Verifying Correctness of a Toy Pong Controller
- 16 Learning State Machine Policies
- 17 Teacher Policy
- 18 Interpretability of State Machine Policies
- 19 Example: Single Group
- 20 Multi-Agent Reinforcement Learning
- 21 Transformer Communication Graph
- 22 Neurosymbolic Transformers
- 23 Learning Algorithm
- 24 Programmatic Attention Rules
- 25 Sparse Communication Structure
- 26 Modular Networks for RNA Splicing
- 27 Conclusion
