Abstract

1. Behavioural priors: Learning to search efficiently in action planningAapo HyvärinenDepts of Computer Science and PsychologyUniversity of Helsinki

2. Abstract • Prior knowledge important in perception • What kinds of objects/scenes are typical/frequent? • Formalized as prior probabilities in Bayesian inference • We propose same needed in action planning • What kind of action sequences are typically good? • Number of possible action sequences large:computationally more efficient to constrain search those which are typically good

3. Basic framework: Planning • Thoroughly investigated in classic AI • Agent is in state A and wants to state B:What sequence of actions is needed? • Agent assumed to have a world model • Exponential explosion in computation:for a actions and t time steps, at possibilities • Exhaustive search impossible

4. Biological agents are different • A biological agent faces the same planning problems many times • Moving the same limbs • Navigation in the same environment • Manipulating similar objects, etc. • Good action sequences obey regularities, due to the physical structure of the world • “Good” means action sequences selected by careful, computationally intensive, planning

5. Learning regularities aids in planning • Initially, agent considers whole search space • It can learn from information on which action sequences were good / typically executed • “Typical” and “good” are strongly correlated because only rather good sequences are executed • Search can then be constrained • Examples of regularities: • No point in moving limb back and forth • Many sequences contain detours • Skills: learning more regularities in particular task

6. A prior model on good sequences • A probabilistic approach: build a model on the statistical structure of those sequences which were executed (= lead to goal, or close) • Use a model which can generate candidate sequences in future planning • E.g. A Markov model • After sufficient experience, search only using action sequences generated by the prior model

7. Simulation 1 • Grid world, actions: “up”, “down”, “left”, “right” • Food randomly scattered • Initially, planning is random • Markov prior learned, used in later test period • Result 1: Markov model learns the “rules” • Do not go back and forth • Do not change direction too often • Result 2: Performance improved

8. Simulation 2 • Using behavioural prior to improve model-free reinforcement learning • Same grid world, one goal • Value function incompletelylearned by Q-learning • After Q-learning, plan to find maximum of value function • Similar results as in Simulation 1 Value function previously learned

9. Related and future work • A lot of work on chunking actions, “macro-actions”: A special form of priors • Options (Sutton et al): probabilistic interpretation also possible? • Case-based planning: different because new action considered also depends on world state • Behavioural priors in • model-free reinforcement learning? • motor control? (motor synergies)

10. Conclusion • Perceptual priors are widely considered important for perception to work • We propose action planning needsbehavioural priors • Priors tell which action sequences are typically useful • Improves planning by constraining search • More simulations needed to verify utility