Reinforcement Learning for Live Musical Agents

Current research programmes in computer music may draw from developments in agent technology; music may provide an excellent test case for agent research. This paper describes the challenge of building agents for concert performance which allow close and rewarding interaction with human musicians. This is easier said than done; the fantastic abilities of human musicians in fluidity of action and cultural reference makes for a difficult mandate. The problem can be cast as that of building an autonomous agent for the (unforgiving) realtime musical environment. Live music is a challenging domain to model, with high dimensionality of descriptions and fast learning, responses and effective anticipation required. A novel symbolic interactive music system called Improvagent is presented as a framework for the testing of reinforcement learning over dynamic state-action case libraries, in a context of MIDI piano improvisation. Reinforcement signals are investigated based on the quality of musical prediction, and on the degree of influence in interaction. The former is found to be less effective than baseline methods of assumed stationarity and of simple nearest neighbour case selection. The latter holds more promise; an agent may be able to assess the value of an action in response to an observed state with respect to the potential for stability, or the promotion of change in future states, enabling controlled musical interaction. 1.