  Legged robots are becoming increasingly powerful and popular in recent years
for their potential to bring the mobility of autonomous agents to the next
level. This work presents a deep reinforcement learning approach that learns a
robust Lidar-based perceptual locomotion policy in a partially observable
environment using Proximal Policy Optimisation. Visual perception is critical
to actively overcome challenging terrains, and to do so, we propose a novel
learning strategy: Dynamic Reward Strategy (DRS), which serves as effective
heuristics to learn a versatile gait using a neural network architecture
without the need to access the history data. Moreover, in a modified version of
the OpenAI gym environment, the proposed work is evaluated with scores over 90%
success rate in all tested challenging terrains.
