Reinforcement Learning Agent Training with Goals for Real World Tasks
  Reinforcement Learning (RL) is a promising approach for solving various
control, optimization, and sequential decision making tasks. However, designing
reward functions for complex tasks (e.g., with multiple objectives and safety
constraints) can be challenging for most users and usually requires multiple
expensive trials (reward function hacking). In this paper we propose a
specification language (Inkling Goal Specification) for complex control and
optimization tasks, which is very close to natural language and allows a
practitioner to focus on problem specification instead of reward function
hacking. The core elements of our framework are: (i) mapping the high level
language to a predicate temporal logic tailored to control and optimization
tasks, (ii) a novel automaton-guided dense reward generation that can be used
to drive RL algorithms, and (iii) a set of performance metrics to assess the
behavior of the system. We include a set of experiments showing that the
proposed method provides great ease of use to specify a wide range of real
world tasks; and that the reward generated is able to drive the policy training
to achieve the specified goal.
