Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction
This work addresses the challenge of sample efficiency in sequential prediction and robotics control for researchers and practitioners, offering an incremental improvement over existing imitation learning methods.
The paper tackles the problem of sequential decision making by proposing AggreVaTeD, a differentiable imitation learning method that leverages near-optimal oracles to achieve faster and better solutions with less training data than reinforcement learning, with theoretical and empirical results showing up to exponentially lower sample complexity and superior performance on tasks like dependency-parsing and robotics control.
Researchers have demonstrated state-of-the-art performance in sequential decision making problems (e.g., robotics control, sequential prediction) with deep neural network models. One often has access to near-optimal oracles that achieve good performance on the task during training. We demonstrate that AggreVaTeD --- a policy gradient extension of the Imitation Learning (IL) approach of (Ross & Bagnell, 2014) --- can leverage such an oracle to achieve faster and better solutions with less training data than a less-informed Reinforcement Learning (RL) technique. Using both feedforward and recurrent neural network predictors, we present stochastic gradient procedures on a sequential prediction task, dependency-parsing from raw image data, as well as on various high dimensional robotics control problems. We also provide a comprehensive theoretical study of IL that demonstrates we can expect up to exponentially lower sample complexity for learning with AggreVaTeD than with RL algorithms, which backs our empirical findings. Our results and theory indicate that the proposed approach can achieve superior performance with respect to the oracle when the demonstrator is sub-optimal.