A Brief Survey of Deep Reinforcement Learning (1708.05866v2)

Abstract: Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly from pixels. Deep reinforcement learning algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of reinforcement learning, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep reinforcement learning, including the deep $Q$-network, trust region policy optimisation, and asynchronous advantage actor-critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via reinforcement learning. To conclude, we describe several current areas of research within the field.

A Brief Survey of Deep Reinforcement Learning

The survey paper titled "A Brief Survey of Deep Reinforcement Learning" by Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath provides an encompassing review of the field of Deep Reinforcement Learning (DRL). This paper is structured to offer a foundational understanding of reinforcement learning (RL) and progresses to cover critical advancements in deep learning that have revolutionized RL, enabling its application to a broad array of complex, high-dimensional problems.

The core of RL involves autonomous agents that learn optimal behaviors through trial and error by interacting with their environments. Traditional RL methods struggled with scalability and dimensionality issues, which limited their applicability to low-dimensional problems. However, the combination of deep learning (DL) with RL algorithms has enabled significant advancements, constituting the field of DRL.

Introduction to Reinforcement Learning

The paper begins with an overview of the foundational principles of RL, highlighting the framework of Markov Decision Processes (MDPs). An MDP is defined by states ($\mathcal{S}$), actions ($\mathcal{A}$), transition dynamics ($\mathcal{T}$), reward functions ($\mathcal{R}$), and a discount factor ($\gamma$). The principal objective in RL is to derive an optimal policy ($\pi^*$) that maximizes the expected return.

Value Functions and Policy Search

Two primary categories of RL algorithms are value function methods and policy-based methods. Value functions estimate the expected return of states or state-action pairs, with well-known algorithms such as $Q$-learning and the State-Action-Reward-State-Action (SARSA) algorithm.

Policy search methods, on the other hand, directly optimize policy parameters to maximize returns. This can be achieved using gradient-free approaches, like evolutionary strategies, or gradient-based approaches such as REINFORCE. Actor-Critic methods combine value function and policy search approaches, where the "actor" updates policies using feedback from the "critic".

Deep Reinforcement Learning

DRL has demonstrated significant successes primarily due to the powerful representation learning capabilities of deep neural networks, particularly convolutional neural networks (CNNs). CNNs facilitate efficient processing of high-dimensional inputs, such as raw visual data. The seminal Deep $Q$-Network (DQN) algorithm by Mnih et al. showcased this by achieving human-level competency across various Atari 2600 games using raw pixel inputs. The DQN leverages experience replay and target networks to address instability and improve sample efficiency.

Augmentations of the $Q$-function such as double-$Q$ learning, dueling networks, and distributional $Q$-learning have further enhanced the efficacy and stability of DRL algorithms. Continuous control problems have also been approached with adaptively structured algorithms such as Normalized Advantage Functions (NAF) and deterministic policy gradients (DPG).

Model-Based Methods and Efficiency

Model-based DRL methods, which learn predictive models of the environment, enable efficient planning and exploration by simulating interactions internally. This significantly reduces the need for sample interactions with the actual environment, making these methods well-suited for tasks like robotics where real-world exploration is expensive. Integration of deep models in these methods has further enriched their capabilities, although their high sample complexity remains a challenge.

Exploration and Hierarchical Learning

Efficient exploration remains a significant challenge in DRL. Strategies such as bootstrapped DQN, upper confidence bounds (UCB), and intrinsic motivation guide agents to explore efficiently. Furthermore, hierarchical reinforcement learning (HRL) modularizes policies into sub-policies or options, enhancing learning in complex environments through structured policy hierarchies.

Multi-Agent Systems and Imitation Learning

Multi-agent RL (MARL) introduces additional complexity by incorporating the interplay between multiple learning agents. Differentiable communication channels among agents can foster more effective co-operative strategies. Additionally, imitation learning and inverse RL (IRL) leverage expert demonstrations to expedite policy learning and optimize performance through inferred reward structures.

Challenges and Future Directions

Despite the substantial strides made, DRL faces numerous challenges before achieving broader applicability. Improved theoretical understanding of neural network properties within RL, better generalization techniques, more sample-efficient algorithms, and integration with other AI methodologies are pivotal for future advancements. Model-based approaches need improved data efficiency, and transfer learning methods need to aid in adapting models to new tasks and environments seamlessly.

Conclusion

The survey concludes by recognizing DRL's transformative impact on AI. It posits that a deeper integration of DRL with other AI fields could offer more comprehensive, data-efficient, and interpretable solutions. As the AI community continues to address the challenges noted, the potential for DRL to drive the development of more general-purpose, autonomous agents remains promising.