Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction (1703.01030v1)

Abstract: 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.

• The paper introduces Aggre VaTeD, a novel imitation learning framework that employs differentiable approximators and cost-to-go oracles to optimize sequential prediction tasks.
• It combines online and natural gradient descent methods, enabling the efficient training of deep neural network policies in complex, partially observable environments.
• Empirical findings reveal that Aggre VaTeD significantly reduces sample complexity and cumulative regret compared to traditional reinforcement learning strategies.

Overview of "Deeply Aggre VaTeD: Differentiable Imitation Learning for Sequential Prediction"

The paper "Deeply Aggre VaTeD: Differentiable Imitation Learning for Sequential Prediction" proposes a novel approach to imitation learning (IL) tailored for sequential prediction tasks, termed Aggre VaTeD, which extends the interactive IL framework with differentiable function approximators. Through the use of policy gradient methods and leveraging cost-to-go oracles, this work aims to achieve efficient learning that surpasses the performance of existing reinforcement learning (RL) methodologies, particularly in contexts where access to near-optimal oracles is available during training.

Methodology

Aggre VaTeD is based on the Imitation Learning framework, which contrasts with conventional RL by exploiting a known oracle to minimize exploration by imitating an expert policy. It introduces two gradient update procedures:

1. Regular Gradient Update utilizing Online Gradient Descent (OGD), which is conducive to differentiable function approximators that manage large parameter spaces.
2. Natural Gradient Descent, inspired by RL optimizations, employing Exponential Gradient Descent (EG) to adjust policies in the IL context.

These procedures allow Aggre VaTeD to handle complex, non-linear policies, including those represented by deep neural networks and LSTM-based structures for partially observable settings.

Empirical and Theoretical Analysis

The authors present both empirical results and theoretical justifications. Empirically, the paper demonstrates how Aggre VaTeD outpeforms traditional RL strategies in various domains:

• Robotics control tasks, where Aggre VaTeD achieves expert or super-expert performance levels with fewer episodes compared to RL.
• Sequential prediction tasks, specifically in dependency parsing of handwritten algebra, showcasing its superior performance in complex, partially observable environments.

The theoretical analysis underscores a crucial point: imitation learning with Aggre VaTeD can exhibit exponentially lower sample complexity compared to RL strategies, highlighting its sample efficiency when optimal cost-to-go oracles are accessible. The paper elaborates on the concept of cumulative regret in learning, illustrating a notable undershoot in regret accumulation for IL when compared to RL, particularly in finite-horizon settings. This emphasizes a polynomial to exponential efficiency gap in favor of IL under specific conditions.

Implications and Speculations for Future Research

The implications of this research extend to various domains requiring sequential decision-making under partial observability or high dimensionality. In practical terms, Aggre VaTeD's efficiency in learning implies reduced computational resources and time in training complex systems, rendering it advantageous in real-world applications such as robotics and language processing.

Theoretically, the work opens avenues for further exploration into the development of more sophisticated IL techniques that blend the strengths of RL and the efficiency of oracle-based approaches. Future developments may witness the integration of more advanced differentiable models with IL frameworks to tackle increasingly complex decision-making scenarios.

In summary, this paper provides a substantial contribution to the field of sequential prediction by refining imitation learning with Aggre VaTeD, presenting a compelling alternative to deep reinforcement learning, particularly when the training environment can leverage oracle guidance.