High-dimensional sparse reward tasks present major challenges for reinforcement learning agents.   In this work we use imitation learning to address two of these challenges:  how to learn a useful representation of the world e.g.  from pixels, and how to explore efficiently given the rarity of a reward signal? We show that adversarial imitation can work well even in this high dimensional observation space. Surprisingly the adversary itself, acting as the learned reward function, can be tiny, comprising as few as 128 parameters, and can be easily trained using the most basic GAN formulation. Our approach removes limitations present in most contemporary imitation approaches: requiring no demonstrator actions (only video), no special initial conditions or warm starts, and no explicit tracking of any single demo. The proposed agent can solve a challenging robot manipulation task of block stacking from only video demonstrations and sparse reward, in which the non-imitating agents fail to learn completely.   Furthermore, our agent learns much faster than competing approaches that depend on hand-crafted, staged dense reward functions, and also better compared to standard GAIL baselines. Finally, we develop a new adversarial goal recognizer that in some cases allows the agent to learn stacking without any task reward, purely from imitation.
Imitation from pixels, with sparse or no reward, using off-policy RL and a tiny adversarially-learned reward function. The paper proposes to use a "minimal adversary" in generative adversarial imitation learning under high-dimensional visual spaces. This paper aims at solving the problem of estimating sparse rewards in a high-dimensional input setting.