  Deep reinforcement learning approaches have been a popular method for visual
navigation tasks in the computer vision and robotics community of late. In most
cases, the reward function has a binary structure, i.e., a large positive
reward is provided when the agent reaches goal state, and a negative step
penalty is assigned for every other state in the environment. A sparse signal
like this makes the learning process challenging, specially in big
environments, where a large number of sequential actions need to be taken to
reach the target. We introduce a reward shaping mechanism which gradually
adjusts the reward signal based on distance to the goal. Detailed experiments
conducted using the AI2-THOR simulation environment demonstrate the efficacy of
the proposed approach for object-goal navigation tasks.
