Learning to reinforcement learn (1611.05763v3)

Abstract: In recent years deep reinforcement learning (RL) systems have attained superhuman performance in a number of challenging task domains. However, a major limitation of such applications is their demand for massive amounts of training data. A critical present objective is thus to develop deep RL methods that can adapt rapidly to new tasks. In the present work we introduce a novel approach to this challenge, which we refer to as deep meta-reinforcement learning. Previous work has shown that recurrent networks can support meta-learning in a fully supervised context. We extend this approach to the RL setting. What emerges is a system that is trained using one RL algorithm, but whose recurrent dynamics implement a second, quite separate RL procedure. This second, learned RL algorithm can differ from the original one in arbitrary ways. Importantly, because it is learned, it is configured to exploit structure in the training domain. We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL. We consider prospects for extending and scaling up the approach, and also point out some potentially important implications for neuroscience.

Learning to Reinforcement Learn

Introduction

The paper explores the concept of deep meta-reinforcement learning (meta-RL), addressing a significant challenge within contemporary deep RL research: the massive volume of training data required for effective task performance. Traditional deep RL systems excel in specific domains like Atari games and Go but fall short in terms of rapid adaptation and flexibility across varied tasks, unlike human learners. This work investigates whether leveraging recurrent neural networks (RNNs) trained with deep RL methods can culminate in self-contained, efficient RL procedures that are adaptive and sample-efficient.

Meta-Learning and Recurrent Neural Networks

The basis of this approach lies in the principle of meta-learning, particularly within RNNs. Instead of conventional, fixed learning rates and exploration strategies, meta-RL systems learn to adapt these parameters dynamically, based on experience with related tasks. Inspired by previous work on supervised meta-learning with RNNs, this paper extends such methodologies to the domain of reinforcement learning. Here, RNNs trained via deep RL algorithms implement a learned RL procedure, distinctly different and potentially more efficient than the original training algorithm, especially when tuned to the structural regularities of the task environment.

Methodology and Experiments

Bandit Problems

The authors initially test their hypothesis on bandit problems, evaluating how meta-RL can exploit the structure of the environment:

1. Independent Arms Bandits: Meta-RL systems demonstrated superior performance compared to Thompson sampling and UCB, although they did not surpass the optimal Gittins index.
2. Dependent Arms Bandits: The system effectively utilized correlations between arms, outperforming standard algorithms not designed for such dependencies. The meta-RL agent displayed adaptability across varying degrees of task difficulty.
3. Exploration Costs in Dependent Arms: The system adopted strategies involving immediate exploratory actions for long-term reward optimization, outperforming standard algorithms.
4. Restless Bandits: Meta-RL systems adjusted their learning rates based on inferred environmental volatility, proving more adept than fixed learning rate models, including UCB and Thompson sampling.

Markov Decision Problems

To analyze more complex task structures, the researchers conducted experiments involving intricate decision-making tasks:

1. Two-Step Task: Commonly used in neuroscience to discern model-free vs. model-based control strategies, the meta-RL agent exhibited behavior commonly associated with model-based control despite being trained via a model-free RL algorithm.
2. Harlow’s Task: In a visually-rich environment akin to Harlow’s learning experiments with monkeys, meta-RL agents learned abstract task structures, demonstrating impressive one-shot learning paradigms by leveraging complex visual stimuli.

Discussion and Implications

This exploration into deep meta-RL displays several pivotal outcomes:

1. Adaptation and Efficiency: By learning task-specific regularities, meta-RL systems adjust their intra-task learning algorithms, achieving higher sample efficiency and adaptability compared to traditional fixed RL algorithms.
2. Model-Free to Model-Based Emergence: Despite training with standard model-free algorithms, meta-RL exhibits behavior mirroring model-based control, indicating an inherent capacity to exploit structured environmental information.
3. Scalability: The success of meta-RL in more complex environments, including visual tasks and extended time horizons, points toward its potential scalability. Enhanced architectures with auxiliary memory mechanisms could further bolster this scalability.

Future Directions

The implications of meta-RL extend beyond AI research into cognition and neuroscience. As deep meta-RL potentially elucidates the parallel between computational and biological learning systems, it warrants further investigation into how neural architectures can rapidly adapt through learned biases. Moreover, incorporating more complex, real-world task environments, and varying reward structures, could further enhance the robustness and efficiency of meta-RL systems, paving the way for more generalized AI capable of human-like flexibility and intuition.

Conclusion

The work convincingly demonstrates that meta-RL systems, trained with deep RL methods on recurrent neural networks, effectively learn to implement unique RL procedures that are adaptive, efficient, and exploit task-specific regularities. This approach thus marks a significant step toward developing AI systems capable of rapid task adaptation and flexible learning, aligning closer with the learning paradigms observed in human cognition.