1. Intelligent Agents: Technology and Applications Multi-agent Learning IST 597B Spring 200 3 John Yen

3. Multi-Agent Learning

8. Predator/Pray (Pursuit) Domain

10. Taxonomy of MAS

11. Taxonomy of MAS

28. 1: Benevolence vs. Competitiveness

46. The Q value R: Reward P xy : The probability of reaching state y from x by taking action action alpha. Gamma: Discount factor (between 0 and 1). V*(y): The expected total discounted return starting in y following the policy *. Policy: a sequence of actions.

47. The Expected Total Discount Return V for a state is the maximal Q value among all actions that can be taken at the state (following the rest of the policy).

49. Learning Rule for Q value Alpha: learning rate

51. Probability for the agent to select action a i based on Q values T: “temperature” parameter to determine the randomness of decisions.

52. Towards Collaborative and Adversarial Learning A Case Study in Robotic Soccer Peter Stone & Manuela Veloso

54. Simple Behavior

56. Parameters

58. Neural Network

59. Results

64. Cooperative Learning

65. Adversarial Learning