Our research has been focussed on multi-agent learning in complex, collaborative and adversarial environments. Our general approach, called layered learning, is based on the premise that realistic domains are too complex for learning mappings directly from sensor inputs to actuator outputs. Instead, intermediate domain-dependent skills should be learned in a bottom-up hierarchical fashion . We implemented TPOT-RL as the current highest layer of a layered learning system in the RoboCup soccer server .
The soccer server used at RoboCup-97  is a much more complex domain than has previously been used for studying multi-agent policy learning. With 11 players on each team controlled by separate processes; noisy, low-level, real-time sensors and actions; limited communication; and a fine-grained world state model including hidden state, the RoboCup soccer server provides a framework in which machine learning can improve performance. Newly developed multi-agent learning techniques could well apply in real-world domains.
A key feature of the layered learning approach is that learned skills at lower levels are used to train higher-level skills. For example, we used a neural network to help players learn how to intercept a moving ball. Then, with all players using the learned interception behavior, a decision tree (DT) enabled players to estimate the likelihood that a pass to a given field location would succeed. Based on almost 200 continuous-valued attributes describing teammate and opponent positions on the field, players learned to classify the pass as a likely success (ball reaches its destination or a teammate gets it) or likely failure (opponent intercepts the ball). Using the C4.5 DT algorithm , the classifications were learned with associated confidence factors. The learned behaviors proved effective both in controlled testing scenarios [14, 16] and against other previously-unseen opponents in an international tournament setting .
These two previously-learned behaviors were both trained off-line in limited, controlled training situations. They could be trained in such a manner due to the fact that they only involved a few players: ball interception only depends on the ball's and the agent's motions; passing only involves the passer, the receiver, and the agents in the immediate vicinity. On the other hand, deciding where to pass the ball during the course of a game requires training in game-situations since the value of a particular action can only be judged in terms of how well it works when playing with particular teammates against particular opponents. For example, passing backwards to a defender could be the right thing to do if the defender has a good action policy, but the wrong thing to do if the defender is likely to lose the ball to an opponent.
Although the DT accurately predicts whether a player can execute a pass, it gives no indication of the strategic value of doing so. But the DT reduces a detailed state description to a single continuous output. It can then be used to drastically reduce the complex state and provide great generalization. In this work we use the DT as the crucial action-dependent feature function e in TPOT-RL.