Transfer Learning in Sequential Decision Problems: A Hierarchical Bayesian Approach Aaron Wilson, Alan Fern, Prasad Tadepalli School of EECS Oregon State University

Markov Decision Processes MDP M : R : Policy Seek optimal policy: Environment Agent

Multi Task Reinforcement Learning (MTRL) Given: A sequence of Markov Decision Processes drawn from an unknown distribution D. Goal: Leverage past experience to improve performance on new MDPs drawn from D. Environment M1Environment M2Environment Mn

MTRL Problem Tasks have hierarchical relationships. Set of classes (unknown to the agent). Natural means of transfer (class discovery).

Hierarchical Bayesian Modeling Foundation:  Dirichlet Process Models  Unknown number of classes.  Discover hierarchical structure. Explicit formulation of Uncertainty  Adapt machinery to the RL setting.  Well justified transfer for RL problems.

Basic Hierarchical Transfer Process Process Inference Select Actions (Bayesian RL) Compute Posterior Select Best Hierarchy

Model-Based Multi-Task RL  Prior model for domain models.  Action selection: Thompson sampling Planning Policy-Based Multi-Task RL  Prior for policy parameters.  Action selection: Bayesian Policy Search algorithm. Hierarchical Bayesian Transfer for RL

Model-Based MTRL Explicitly Model the Generative Process D Hierarchy represents classes of MDPs. Class Prior Estimate D

Action Selection: Exploit estimate of D Exploit the refined prior (class information).  Sample the MDPs using Thompson Sampling.  Plan with the sampled model (Value Iteration). Compute Posterior Plan

Domain 1 State is a bit vector: True reward function: Set of 20 test maps. State

Domain 1 No Transfer 16 previous tasks

Policy-Based MTRL Policy prior. Infer policy components. Hierarchy represents reusable policy components. Class Prior Estimate H

Consider Wargus RTS Multiple Unit types. Units fulfill tactical roles. Roles are useful in multiple maps.  Simple->hard instances Hierarchical policy prior.  Facilitate reuse of roles.

Role Based Policies Set of Roles.  Vectors of policy parameters.  Who to attack. Set of role assignments. A strategy for assigning agents to roles.  Assignment depends on state features. Executing role-based policy  1. Make the assignment  2. Each agent selects action

Transfer of Role-Based Policies Bayesian Policy Search  Learns Individual Role parameters. Role assignment function. Assignments of agents to roles. Sample role-based policies  Construct an artificial distribution [Hoffman et. al. NIPS 2007, Muller Bayes Stats.1999]  Search using stochastic simulation  Model free. Bayesian Policy Search

Experiments Tactical battles in Wargus Transfer given expert examples. Learning without expert examples.

Transfer from expert play.

Transfer from self play Use BPS on Training Map 1. Transfer to new map.

Conclusion Hierarchical Bayesian Modeling for RL Transfer  Model-Based MTRL Learn classes of domain models. Transfer: Improved priors for model-based Bayesian RL.  Policy-Based MTRL Learn re-usable policies. Transfer: Recombine learned policy components in new tasks. Solved tactical games in Wargus

Thank You

Outline Multi-Task Reinforcement Learning (RL).  Markov Decision Processes.  Multi-task RL setting Policy-Based Multi-task RL  Discover classes of policy components.  Bayesian Policy Search Algorithm. Conclusion

Policy-Based MTRL Observed property:  Bags of trajectories. Transfer:  Classes of policy components Means of exploiting transferred information:  Recombine existing components in new tasks. Consequence:  Components reused to learn hard tasks.

Outline Markov Decision Processes Bayesian Model Based Reinforcement Learning Multi Task Reinforcement Learning (MTRL) Modeling the MTRL Problem MTRL Transfer Algorithm  Estimating parameters of the generative process.  Action Selection. Results Conclusion

Bayesian Model Based RL Given prior: Plan using updated model. 1. Most work uses uninformed priors. 2. Selection of prior not supported by data. 3. Priors do not facilitate transfer. Environment