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Office World Experiments
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Classroom Contents
Formal Languages and Automata for Reward Function Specification and Efficient Reinforcement Learning
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- 1 Intro
- 2 Acknowledgements
- 3 Reinforcement Learning (RL)
- 4 Challenges of Real-World RL
- 5 Goals and Preferences
- 6 Linear Temporal Logic (LTL) A compelling logic to express temporal properties of traces.
- 7 Challenges to RL
- 8 Toy Problem Disclaimer
- 9 Running Example
- 10 Decoupling Transition and Reward Functions
- 11 The Rest of the Talk
- 12 Define a Reward Function using a Reward Machine
- 13 Reward Function Vocabulary
- 14 Simple Reward Machine
- 15 Reward Machines in Action
- 16 Other Reward Machines
- 17 Q-Learning Baseline
- 18 Option-Based Hierarchical RL (HRL)
- 19 HRL with RM-Based Pruning (HRL-RM)
- 20 HRL Methods Can Find Suboptimal Policies
- 21 Q-Learning for Reward Machines (QRM)
- 22 QRM In Action
- 23 Recall: Methods for Exploiting RM Structure
- 24 5. QRM + Reward Shaping (QRM + RS)
- 25 Test Domains
- 26 Test in Discrete Domains
- 27 Office World Experiments
- 28 Minecraft World Experiments
- 29 Function Approximation with QRM
- 30 Water World Experiments
- 31 Creating Reward Machines
- 32 Reward Specification: one size does not fit all
- 33 1. Construct Reward Machine from Formal Languages
- 34 Generate RM using a Symbolic Planner
- 35 Learn RMs for Partially-Observable RL
