Exploring Effects of Intrinsic Motivation in Reinforcement Learning Agents

We explore Intrinsic Motivation as a reward framework for learning how to perform complicated tasks. Most reinforcement learning tasks assume the existence of a critic who rewards the agent for its actions. However, taking inspiration for biological agents, we can say that the real critic is the agent itself. We experiment with a model where the rewards are generated by the agent using a process which models the emotion of surprisal. The agent is modeled as intrinsically motivated to look for surprising events. We demonstrate how the agent learns under such a framework. Results show that even in the absence of any external reward, the agent is able to learn skills and perform complicated tasks.