AD-VAT: AN ASYMMETRIC DUELING MECHANISM FOR LEARNING VISUAL ACTIVE TRACKING
Visual Active Tracking (VAT) aims at following a target object by autonomously controlling the motion system of a tracker given visual observations. Previous work has shown that the tracker can be trained in a simulator via reinforcement learning and deployed in real-world scenarios. However, during training, such method requires manually specifying the moving path of the target object to be tracked, which cannot ensure the tracker's generalization on unseen object moving patterns. To learn a robust tracker for VAT, in this paper, we propose a novel adversarial RL method which adopts an Asymmetric Dueling mechanism, referred to as AD-VAT. In AD-VAT, both the tracker and the target are approximated by end-to-end neural networks, and are trained via RL in a dueling/competitive manner: i.e., the tracker intends to lockup the target, while the target tries to escape from the tracker. They are asymmetric in that the target is aware of the tracker, but not vice versa. Specifically, besides its own observation, the target is fed with the tracker's observation and action, and learns to predict the tracker's reward as an auxiliary task. We show that such an asymmetric dueling mechanism produces a stronger target, which in turn induces a more robust tracker. To stabilize the training, we also propose a novel partial zero-sum reward for the tracker/target. The experimental results, in both 2D and 3D environments, demonstrate that the proposed method leads to a faster convergence in training and yields more robust tracking behaviors in different testing scenarios. For supplementary videos, see: https://www.youtube. com/playlist?list=PL9rZj4Mea7wOZkdajK1TsprRg8iUf51BS
This paper presents a simple multi-agent Deep RL task where a moving tracker tries to follow a moving target. The tracker receives, from its own perspective, partially observed visual information o_t^{alpha} about the target (e.g., an image that may show the target) and the target receives both observations from its own perspective o_t^{beta} and a copy of the information from the tracker's perspective. Both agents are standard convnet + LSTM neural architectures trained using A3C and are evaluated in 2D and 3D environments. The reward function is not completely zero-sum, as the tracked agent's reward vanishes when it gets too far from a reference point in the maze.
The work is very incremental over Luo et al (2018) "End-to-end Active Object Tracking and Its Real-world Deployment via Reinforcement Learning", as the only two additions are extra observations o_t^{alpha} for the target, and a reward function that has a fudge factor when the target gets too far away. Citing Sun Tzu's "Art of War" (please use the correct citation format) is not convincing enough for adding the tracker's observations as inputs for the target agent. Should not the asymmetrical relationship work the other way round, with the tracker knowing more about the target?
Experiments are conducted using two baselines for the target agent, one a random walk and another an agent that navigates to a target according to a shortest path planning algorithm. The ablation study shows that the tracker-aware observations and a target's reward structure that penalizes when it gets too far do help the tracker's performance, and that training the target agent helps the tracker agent achieve higher scores. The improvement is however quite small and the task is ad-hoc.
 
The paper would have benefitted from a proper analysis of the trajectories taken by the adversarial target as opposed to the heuristic ones, and from comparison with non-RL state-of-the-art on tracking tasks. Further multi-agent tasks could also have been considered, such as capture the flag tasks as in "Human-level performance in first-person multiplayer games with population-based deep reinforcement learning".