Reinforcement learning trains models by trial and error. In batch reinforcement learning (BRL), models learn by observing many demonstrations by a variety of actors. For instance, a robot might learn how to fix ingrown toenails by watching hundreds of surgeons perform the procedure. But what if one doctor is handier with a scalpel while another excels at suturing? A new method lets models absorb the best skills from each.

What’s new: Ajay Mandlekar and collaborators at Nvidia, Stanford, and the University of Toronto devised a BRL technique that enables models to learn different portions of a task from different examples. This way, the model can gain useful information from inconsistent examples. Implicit Reinforcement without Interaction at Scale (IRIS) achieved state-of-the-art BRL performance in three tasks performed in a virtual environment.

Key insight: Learning from demonstrations is a double-edged sword. An agent gets to see how to complete a task, but the scope of its action is limited to the most complete demonstration of a given task. IRIS breaks down tasks into sequences of intermediate subgoals. Then it performs the actions required to accomplish each subgoal. In this way, the agent learns from the best parts of each demonstration and combines them to accomplish the task.

How it works: IRIS includes a subgoal selection model that predicts intermediate points on the way to accomplishing an assigned task. These subgoals are defined automatically by the algorithm, and may not correspond to parts of a task as humans would describe them. A controller network tries to replicate the optimal sequence of actions leading to a given subgoal.

The subgoal selection model is made up of a conditional variational autoencoder that produces a set of possible subgoals and a value function (trained via a BRL version of Q-learning) that predicts which next subgoal will lead to the highest reward.
The controller is a recurrent neural network that decides on the actions required to accomplish the current subgoal. It learns to predict how demonstrations tend to unfold, and to imitate short sequences of actions from specific demonstrations.
Once it’s trained, the subgoal selection model determines the next subgoal. The controller takes the requisite actions. Then the subgoal selection model evaluates the current state and computes a new subgoal, and so on.

Results: In the Robosuite’s lifting and pick-and-place tasks, previous state-of-the-art BRL approaches couldn’t pick up objects reliably, nor place them elsewhere at all. IRIS learned to pick up objects with over 80 percent success and placed them with 30 percent success.

Why it matters: Automatically identifying subgoals has been a holy grail in reinforcement learning, with active research in hierarchical RL and other areas. The method used in this paper applies to relatively simple tasks where things happen in a predictable sequence (such as picking and then placing), but might be a small step in an important direction.

We’re thinking: Batch reinforcement learning is useful when a model must be interpretable or safe — after all, a robotic surgeon shouldn’t experiment on living patients — but it hasn’t been terribly effective. IRIS could make it a viable option.

Batch Reinforcement Learning (BRL)

Different Skills From Different Demos

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