Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning (1511.06342v4)

Abstract: The ability to act in multiple environments and transfer previous knowledge to new situations can be considered a critical aspect of any intelligent agent. Towards this goal, we define a novel method of multitask and transfer learning that enables an autonomous agent to learn how to behave in multiple tasks simultaneously, and then generalize its knowledge to new domains. This method, termed "Actor-Mimic", exploits the use of deep reinforcement learning and model compression techniques to train a single policy network that learns how to act in a set of distinct tasks by using the guidance of several expert teachers. We then show that the representations learnt by the deep policy network are capable of generalizing to new tasks with no prior expert guidance, speeding up learning in novel environments. Although our method can in general be applied to a wide range of problems, we use Atari games as a testing environment to demonstrate these methods.

• The paper introduces the Actor-Mimic method that uses policy and feature regression to train a single network for handling multiple tasks.
• Key experiments on Atari games show near-expert performance with significantly fewer training frames across various tasks.
• The study demonstrates accelerated learning in transfer tasks, offering a promising route to reduce computational costs in RL.

Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning

The paper "Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning" by Parisotto, Ba, and Salakhutdinov focuses on enhancing the capabilities of reinforcement learning (RL) agents through multitask and transfer learning. The authors introduce the Actor-Mimic network, which leverages model compression and deep reinforcement learning to train a single policy network capable of handling multiple tasks simultaneously and generalizing to new tasks.

Core Contributions

The primary contribution of this research is the Actor-Mimic method, which is rooted in two key objectives: policy regression and feature regression. The policy regression objective aims to enable a student (Actor-Mimic Network, or AMN) to mimic the softmax policy outputs of expert DQNs. This approach enhances stability by focusing more on policy mimicry than direct value mimicry, which can scale variably across tasks. The feature regression adds another layer of guidance by aligning the AMN's intermediate feature representations with those of expert networks.

Experimental Setup and Results

The experiments utilized Atari games from the Arcade Learning Environment (ALE) to validate the Actor-Mimic method. The paper presents a robust performance evaluation across multitask learning and transfer learning scenarios.

• Multitask Learning: Through empirical evaluation, the AMN demonstrated close-to-expert performance across eight games with significantly reduced training frames compared to expert DQNs. Notably, the AMN showed enhanced consistency and partially outperformed experts in games like Atlantis.
• Transfer Learning: For transfer tasks, larger AMNs showed substantial improvements in learning speed for games like Breakout, showcasing accelerated convergence compared to a randomly initialized DQN. However, the effectiveness of transfer varied, indicating potential for further research in minimizing negative transfer.

Theoretical Insights

The paper offers theoretical analysis affirming the convergence properties of the Actor-Mimic algorithm. It establishes that under certain conditions, the learning process converges consistently to a solution, reinforcing its reliability.

Practical Implications and Future Directions

The implications of Actor-Mimic span both practical and theoretical domains:

• Practical: It reduces training times substantially by using prior knowledge, thereby lowering computational costs. This efficiency is crucial for applications requiring rapid adaptation to diverse environments.
• Theoretical: The approach contributes to a deeper understanding of multitask and transfer learning dynamics in deep RL, suggesting potential pathways for developing more generalized learning systems.

Looking forward, an intriguing direction for future research is the targeted selection of source tasks to optimize knowledge transfer. Exploring automated methods to discern task similarities could further enhance learning outcomes and mitigate issues of negative transfer evident in certain tasks.

In summary, the Actor-Mimic framework represents a significant stride towards more versatile and efficient RL agents, capable of leveraging past knowledge across multiple domains to perform effectively in new, unseen environments.