(Georgia Institute of Technology, 2017-08)
Sood, Srijan
Reinforcement learning is a powerful mechanism for training artificial and real-world agents to perform tasks. Typically, one can define a task for an agent by simply specifying rewards that reflect the agent’s performance. However, each time the task changes, one must develop a new reward specification. Our work aims to remove the necessity of designing rewards in tasks consisting of visual inputs. When humans are learning to complete tasks, we often look to other sources for inspiration or instruction. Even if the representation is different from our own, we can adapt our own representation to the task representation. This motivates our own work, where we present tasks to an agent that are from an environment different than its own. We compare the cross-domain goal representation with the agents representation to form Cross-Domain Perceptual Reward (CDPR) functions and show that these enable the agent to successfully complete its task.