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We present a novel approach to improve the performance of deep reinforcementlearning (DRL) based outdoor robot navigation systems. Most, existing DRLmethods are based on carefully designed dense reward functions that learn theefficient behavior in an environment. We circumvent this issue by working onlywith sparse rewards (which are easy to design), and propose a novel adaptiveHeavy-Tailed Reinforce algorithm for Outdoor Navigation called HTRON. Our mainidea is to utilize heavy-tailed policy parametrizations which implicitly induceexploration in sparse reward settings. We evaluate the performance of HTRONagainst Reinforce, PPO and TRPO algorithms in three different outdoorscenarios: goal-reaching, obstacle avoidance, and uneven terrain navigation. Weobserve in average an increase of 34.41% in terms of success rate, a 15.15%decrease in the average time steps taken to reach the goal, and a 24.9%decrease in the elevation cost compared to the navigation policies obtained bythe other methods. Further, we demonstrate that our algorithm can betransferred directly into a Clearpath Husky robot to perform outdoor terrainnavigation in real-world scenarios.

HTRON:Efficient Outdoor Navigation with Sparse Rewards via Heavy Tailed Adaptive Reinforce Algorithm