1 / 24

Encoding Robotic Sensor States for Q-Learning using the Self-Organizing Map

Gabriel J. Ferrer Department of Computer Science Hendrix College. Encoding Robotic Sensor States for Q-Learning using the Self-Organizing Map. Outline. Statement of Problem Q-Learning Self-Organizing Maps Experiments Discussion. Statement of Problem. Goal Make robots do what we want

stan
Download Presentation

Encoding Robotic Sensor States for Q-Learning using the Self-Organizing Map

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Gabriel J. Ferrer Department of Computer Science Hendrix College Encoding Robotic Sensor Statesfor Q-Learning using the Self-Organizing Map

  2. Outline • Statement of Problem • Q-Learning • Self-Organizing Maps • Experiments • Discussion

  3. Statement of Problem • Goal • Make robots do what we want • Minimize/eliminate programming • Proposed Solution: Reinforcement Learning • Specify desired behavior using rewards • Express rewards in terms of sensor states • Use machine learning to induce desired actions • Target Platform • Lego Mindstorms NXT

  4. Robotic Platform

  5. Experimental Task • Drive forward • Avoid hitting things

  6. Q-Learning • Table of expected rewards (“Q-values”)‏ • Indexed by state and action • Algorithm steps • Calculate state index from sensor values • Calculate the reward • Update previous Q-value • Select and perform an action • Q(s,a) = (1 - α) Q(s,a) + α (r + γ max(Q(s',a)))‏

  7. Q-Learning and Robots • Certain sensors provide continuous values • Sonar • Motor encoders • Q-Learning requires discrete inputs • Group continuous values into discrete “buckets” • [Mahadevan and Connell, 1992] • Q-Learning produces discrete actions • Forward • Back-left/Back-right

  8. Creating Discrete Inputs • Basic approach • Discretize continuous values into sets • Combine each discretized tuple into a single index • Another approach • Self-Organizing Map • Induces a discretization of continuous values • [Touzet 1997] [Smith 2002]

  9. Self-Organizing Map (SOM)‏ • 2D Grid of Output Nodes • Each output corresponds to an ideal input value • Inputs can be anything with a distance function • Activating an Output • Present input to the network • Output with the closest ideal input is the “winner”

  10. Applying the SOM • Each input is a vector of sensor values • Sonar • Left/Right Bump Sensors • Left/Right Motor Speeds • Distance function is sum-of-squared-differences

  11. SOM Unsupervised Learning • Present an input to the network • Find the winning output node • Update ideal input for winner and neighbors • weightij = weightij + (α * (inputij – weightij)) • Neighborhood function

  12. Experiments • Implemented in Java (LeJOS 0.85) • Each experiment • 240 seconds (800 Q-Learning iterations) • 36 States • Three actions • Both motors forward • Left motor backward, right motor stopped • Left motor stopped, right motor backward

  13. Rewards • Either bump sensor pressed: 0.0 • Base reward: • 1.0 if both motors are going forward • 0.5 otherwise • Multiplier: • Sonar value greater than 20 cm: 1 • Otherwise, (sonar value) / 20

  14. Parameters • Discount (γ): 0.5 • Learning rate (α): • 1/(1 + (t/100)), t is the current iteration (time step) • Used for both SOM and Q-Learning [Smith 2002] • Exploration/Exploitation • Epsilon = α/4 • Probability of random action • Selected using weighted distribution

  15. Experimental Controls • Q-Learning without SOM • Qa States • Current action (1-3) • Current bumper states • Quantized sonar values (0-19 cm; 20-39; 40+) • Qb States • Current bumper states • Quantized sonar values (9) (0-11 cm…; 84-95; 96+)

  16. SOM Formulations • 36 Output Nodes • Category “a”: • Length-5 input vectors • Motor speeds, bumper values, sonar value • Category “b”: • Length-3 input vectors • Bumper values, sonar value • All sensor values normalized to [0-100]

  17. SOM Formulations • QSOM • Based on [Smith 2002] • Gaussian Neighborhood • Neighborhood size is one-half SOM width • QT • Based on [Touzet 1997] • Learning rate is fixed at 0.9 • Neighborhood is immediate Manhattan neighbors • Neighbor learning rate is 0.4

  18. Quantitative Results

  19. Qualitative Results • QSOMa • Motor speeds ranged from 2% to 50% • Sonar values stuck between 90% and 94% • QSOMb • Sonar values range from 40% to 95% • Best two runs arguably the best of the bunch • Very smooth SOM values in both cases

  20. Qualitative Results • QTa • Sonar values ranged from 10% to 100% • Still a weak performer on average • Best performer similar to QTb • QTb • Developed bump-sensor oriented behavior • Made little use of sonar • Highly uneven SOM values in both cases

  21. Experimental Area

  22. First Movie • QSOMb • Strong performer (Reward: 661.89) • Minimum sonar value: 43.35% (110 cm)

  23. Second Movie • Also QSOMb • Typical bad performer (Reward: 451.6) • Learns to avoid by always driving backwards • Baseline “not-forward” reward: 400.0 • Minimum sonar value: 57.51% (146 cm) • Hindered by small filming area

  24. Discussion • Use of SOM on NXT can be effective • More research needed to address shortcomings • Heterogeneity of sensors is a problem • Need to try NXT experiments with multiple sonars • Previous work involved homogeneous sensors • Approachable by undergraduate students • Technique taught in junior/senior AI course

More Related