TendencyRL: Multistage Discriminative Hints for Efficient GoalOriented Reverse Curriculum Learning Deep reinforcement learning algorithms have been proven successful in a variety of simulation tasks with dense reward feedback. However, realworld RL applications, e.g. robotic manipulation, remain challenging as most of them are multistage and a positive reward can only be received when the final goal is accomplished. In this work, we propose a potential solution to such problems with the introduction of an experiencebased tendency reward shaping mechanism, which provides the robot with additional hints based on a discriminative learning on past experience. The reward along with a stageawareness network help accelerate solving a multistage task split into shorter phases in a reverse curriculum learning manner. We extensively study the advantages of TRL on the standard longterm goaloriented robotics domains such as pickandplace, and show that TRL performs more efficiently and robustly than prior approaches in tasks with large state space. In addition, we demonstrate that TRL can solve difficult robot manipulation challenges directly from perception.

Tactical Reward Shaping: Bypassing Reinforcement Learning with StrategyBased Goals Deep Reinforcement Learning (DRL) has shown its promising capabilities to learn optimal policies directly from trial and error. However, learning can be hindered if the goal of the learning, defined by the reward function, is not optimal. We demonstrate that by setting the goaltarget of competition in a counterintuitive but intelligent way, instead of heuristically trying solutions through many hours, the DRL simulation can quickly converge into a winning strategy. The ICRADJI RoboMaster AI Challenge is a game of cooperation and competition between robots in a partially observable environment, quite similar to the CounterStrike game. Unlike the traditional approach to games, where the reward is given at winning the match or hitting the enemy, our DRL algorithm rewards our robots when in a geometricstrategic advantage, which implicitly increases the winning chances. Furthermore, we use Deep Q Learning (DQL) to generate multiagent paths for moving, which improves the cooperation between two robots by avoiding collision. Finally, we implement a variant A* algorithm with the same implicit geometric goal as DQL and compare results. We conclude that a wellset goal can put in question the need for learning algorithms, with geometricbased searches outperforming DQL in many orders of magnitude.