  Demonstration is an appealing way for humans to provide assistance to
reinforcement-learning agents. Most approaches in this area view demonstrations
primarily as sources of behavioral bias. But in sparse-reward tasks, humans
seem to treat demonstrations more as sources of causal knowledge. This paper
proposes a framework for agents that benefit from demonstration in this
human-inspired way. In this framework, agents develop causal models through
observation, and reason from this knowledge to decompose tasks for effective
reinforcement learning. Experimental results show that a basic implementation
of Reasoning from Demonstration (RfD) is effective in a range of sparse-reward
tasks.
