1. Department of Computer Science Ben-Gurion University
Decision Process with Non-Markovian Reward
Benjamin Berend & Amihay Elboher
Supervisor:
Prof. Ronen Brafman
Department of Computer Science Ben-Gurion University

2. Graphical infrastructure for running, planning and learning algorithms
Goal: clean the stains and collect the fruits to the basket

3. RL – reinforcement learning
Reinforcement learning- optimizing a behavior by learning from retributions
RL – reinforcement learning
Known Environment
Unknown Environment
Policy Iteration
Q-Learning
R-Max
Automata Learning

4. Known Environment MDP – Markov Decision Process
In a known environment, the MDP is fully observable.

5. ? ! Q-Learning Experience
The learning is experience based: the agent starts from any policy, and adjusts it’s behavior according the rewards it gets.

6. Automata Learning Sample: 1, 10, 100
0,1
In order to learn a non-Markovian reward we construct an automaton that
accepts all “words” that lead to the reward. The algorithm finds the “most
logical” automaton and combines it in the state. *Σ
Sample: 1, 10, 100 1
*10 1
1, 10, 100 }}