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Title: Goal-conditioned Imitation Learning

Paper content:
Designing rewards for Reinforcement Learning (RL) is challenging because it needs to convey the desired task, be efficient to optimize, and be easy to compute.
The latter is particularly problematic when applying RL to robotics, where detecting whether the desired configuration is reached might require considerable supervision and instrumentation.
Furthermore, we are often interested in being able to reach a wide range of configurations, hence setting up a different reward every time might be unpractical.
Methods like Hindsight Experience Replay (HER) have recently shown promise to learn policies able to reach many goals, without the need of a reward.
Unfortunately, without tricks like resetting to points along the trajectory, HER might take a very long time to discover how to reach certain areas of the state-space.
In this work we investigate different approaches to incorporate demonstrations to drastically speed up the convergence to a policy able to reach any goal, also surpassing the performance of an agent trained with other Imitation Learning algorithms.
Furthermore, our method can be used when only trajectories without expert actions are available, which can leverage kinestetic or third person demonstration.
Reinforcement Learning (RL) has shown impressive results in a plethora of simulated tasks, ranging from attaining super-human performance in video-games BID18 BID35 and board-games (Silver et al., 2017) , to learning complex locomotion behaviors BID34 BID4 .
Nevertheless, these successes are shyly echoed in real world robotics (Riedmiller et BID36 .
This is due to the difficulty of setting up the same learning environment that is enjoyed in simulation.
One of the critical assumptions that are hard to obtain in the real world are the access to a reward function.
Self-supervised methods have the power to overcome this limitation.A very versatile and reusable form of self-supervision for robotics is to learn how to reach any previously observed state upon demand.
This problem can be formulated as training a goal-conditioned policy BID14 BID27 that seeks to obtain the indicator reward of having the observation exactly match the goal.
Such a reward does not require any additional instrumentation of the environment beyond the sensors the robot already has.
But in practice, this reward is never observed because in continuous spaces like the ones in robotics, the exact same observation is never observed twice.
Luckily, if we are using an off-policy RL algorithm BID17 BID11 , we can "relabel" a collected trajectory by replacing its goal by a state actually visited during that trajectory, therefore observing the indicator reward as often as we wish.
This method was introduced as Hindsight Experience Replay BID0 or HER.In theory these approaches could learn how to reach any goal, but the breadth-first nature of the algorithm makes that some areas of the space take a long time to be learned BID7 .
This is specially challenging when there are bottlenecks between different areas of the statespace, and random motion might not traverse them easily BID5 .
Some practical examples of this are pick-and-place, or navigating narrow corridors between rooms, as illustrated in Fig. 5 in appendix depicting the diverse set of environments we work with.
In both cases a specific state needs to be reached (grasp the object, or enter the corridor) before a whole new area of the space is discovered (placing the object, or visiting the next room).
This problem could be addressed by engineering a reward that guides the agent towards the bottlenecks, but this defeats the purpose of trying to learn without direct reward supervision.
In this work we study how to leverage a few demonstrations that traverse those bottlenecks to boost the learning of goal-reaching policies.Learning from Demonstrations, or Imitation Learning (IL), is a well-studied field in robotics BID15 BID25 BID2 .
In many cases it is easier to obtain a few demonstrations from an expert than to provide a good reward that describes the task.
Most of the previous work on IL is centered around trajectory following, or doing a single task.
Furthermore it is limited by the performance of the demonstrations, or relies on engineered rewards to improve upon them.
In this work we study how IL methods can be extended to the goal-conditioned setting, and show that combined with techniques like HER it can outperform the demonstrator without the need of any additional reward.
We also investigate how the different methods degrade when the trajectories of the expert become less optimal, or less abundant.
Finally, the method we develop is able to leverage demonstrations that do not include the expert actions.
This is very convenient in practical robotics where demonstrations might have been given by a motion planner, by kinestetic demonstrations (moving the agent externally, and not by actually actuating it), or even by another agent.
To our knowledge, this is the first framework that can boost goal-conditioned policy learning with only state demonstrations.
Hindsight relabeling can be used to learn useful behaviors without any reward supervision for goal-conditioned tasks, but they are inefficient when the state-space is large or includes exploration bottlenecks.
In this work we show how only a few demonstrations can be leveraged to improve the convergence speed of these methods.
We introduce a novel algorithm, goal-GAIL, that converges faster than HER and to a better final performance than a naive goal-conditioned GAIL.
We also study the effect of doing expert relabeling as a type of data augmentation on the provided demonstrations, and demonstrate it improves the performance of our goal-GAIL as well as goal-conditioned Behavioral Cloning.
We emphasize that our goal-GAIL method only needs state demonstrations, without using expert actions like other Behavioral Cloning methods.
Finally, we show that goal-GAIL is robust to sub-optimalities in the expert behavior.

assistant: text: We tackle goal-conditioned tasks by combining Hindsight Experience Replay and Imitation Learning algorithms, showing faster convergence than the first and higher final performance than the second.