  Model-free reinforcement learning (RL) is a powerful approach for learning
control policies directly from high-dimensional state and observation. However,
it tends to be data-inefficient, which is especially costly in robotic learning
tasks. On the other hand, optimal control does not require data if the system
model is known, but cannot scale to models with high-dimensional states and
observations. To exploit benefits of both model-free RL and optimal control, we
propose time-to-reach-based (TTR-based) reward shaping, an optimal
control-inspired technique to alleviate data inefficiency while retaining
advantages of model-free RL. This is achieved by summarizing key system model
information using a TTR function to greatly speed up the RL process, as shown
in our simulation results. The TTR function is defined as the minimum time
required to move from any state to the goal under assumed system dynamics
constraints. Since the TTR function is computationally intractable for systems
with high-dimensional states, we compute it for approximate, lower-dimensional
system models that still captures key dynamic behaviors. Our approach can be
flexibly and easily incorporated into any model-free RL algorithm without
altering the original algorithm structure, and is compatible with any other
techniques that may facilitate the RL process. We evaluate our approach on two
representative robotic learning tasks and three well-known model-free RL
algorithms, and show significant improvements in data efficiency and
performance.

Robotics