Using simulated data to train robots


Applying end-to-end learning to solve complex, interactive, pixel-driven control tasks on a robot is an unsolved problem. Deep Reinforcement Learning algorithms are too slow to achieve performance on a real robot, but their potential has been demonstrated in simulated environments. We propose using progressive networks to bridge the reality gap and transfer learned policies from simulation to the real world. The progressive net approach is a general framework that enables reuse of everything from low-level visual features to high-level policies for transfer to new tasks, enabling a compositional, yet simple, approach to building complex skills. We present an early demonstration of this approach with a number of experiments in the domain of robot manipulation that focus on bridging the reality gap. Unlike other proposed approaches, our real-world experiments demonstrate successful task learning from raw visual input on a fully actuated robot manipulator. Moreover, rather than relying on model-based trajectory optimisation, the task learning is accomplished using only deep reinforcement learning and sparse rewards.


It's really difficult to use deep RL to train pixel-driven robots. This paper tries to do so using progressive networks. The paper is useful as it provides a proof-of-concept by which deep RL can be used on a real robot.

Progressive nets are an architecture that connects each layer of previously learnt network columns to each new column. They were used successfully by @rusu16 on to train a model on a number of different Atari games.

In a progressive network (prognet), you initially train a deep network with hidden layers \(h_i^{(1)}\) and parameters \(\Theta^{(1)}\) to convergence. When you switch to a second task, \(\Theta^{(1)}\) is frozen, and a new column with parameters \(\Theta^{(2)}\) is instantiated, with each hidden layer \(h_i^{(2)}\) receiving input from both \(h_{i-1}^{(2)}\) and \(h_{i-1}^{(1)}\) via lateral connections. Effectively, a progresive network is one in which you have \(N\) deep neural networks, each connected laterally. Consequently, we have \(N\) policies, and are thus learning a probability distribution over all states and actions.

One advantage of this is that the columns of a prognet do not have to be identical, which allows us to train a deep neural net using simulation, and then hook the simulated network into the prognet. See figure.

The risk here is that any rewards will be so sparse that it will be impossible to learn effectively. The authors get around that by having the initial policy of the agent identical to the previous column, and then learning on it.

The authors tested the system on a robot trying to pick up a ball. They found a strong increase in performance, and that the prognet was less sensitive to hyperparameter selection.