Evolved Intrinsic Reward Functions for Reinforcement Learning

The reinforcement learning (RL) paradigm typically assumes a class of efficient, general search procedures that search a given reward function that is part of the problem over the space of programs--to search for reward functions. However, in animals, all reward These reward functions operate over the entire state space of signals are generated internally, rather than being received a reinforcement learning problem and, if successful, will be directly from the environment. Furthermore, animals able to quickly and automatically identify relevant variables have evolved motivational systems that facilitate learning by and features of the problem. This will allow the agent to rewarding activities that often bear a distal relationship to outperform an agent that uses the obvious task-based reward the animal's ultimate goals. Such intrinsic motivation can function. The use of genetic programming methods may alleviate cause an agent to explore and learn in the absence of external the difficulty of scaling reward function search and rewards, possibly improving its performance over a set provide a natural way to search through a very expressive of problems.