Deep Reinforcement Learning (1810.06339v1)

Published 15 Oct 2018 in cs.LG and stat.ML

Abstract: We discuss deep reinforcement learning in an overview style. We draw a big picture, filled with details. We discuss six core elements, six important mechanisms, and twelve applications, focusing on contemporary work, and in historical contexts. We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources. Next we discuss RL core elements, including value function, policy, reward, model, exploration vs. exploitation, and representation. Then we discuss important mechanisms for RL, including attention and memory, unsupervised learning, hierarchical RL, multi-agent RL, relational RL, and learning to learn. After that, we discuss RL applications, including games, robotics, NLP, computer vision, finance, business management, healthcare, education, energy, transportation, computer systems, and, science, engineering, and art. Finally we summarize briefly, discuss challenges and opportunities, and close with an epilogue.

Authors (1)

Yuxi Li (45 papers)

Citations (146)

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Summary

An Overview of "Deep Reinforcement Learning" by Yuxi Li

The paper "Deep Reinforcement Learning" by Yuxi Li provides a comprehensive and formal examination of the integration of deep learning with reinforcement learning (RL), outlining the essential elements and mechanisms that have driven recent advancements in this area. The manuscript discusses foundational concepts, presents notable methods and results, and speculates on future applications, offering an extensive resource for experienced researchers interested in deep reinforcement learning (DRL).

Core Elements of Reinforcement Learning

The paper begins by discussing the six core elements of reinforcement learning: value function, policy, reward, model, exploration vs. exploitation, and representation. These elements form the basis for understanding and developing RL systems. The document details how these components function and interrelate, creating a foundation for constructing sophisticated RL algorithms.

Value Function: The paper explores the importance of value functions in predicting future rewards and the learning of optimal policies. Classical and modern algorithms such as Deep Q-Network (DQN), double Q-learning, and distributional value functions are explored, highlighting their impact on stability and performance improvements.
Policy: Various policy-based methods are examined, including the relevance of policy gradient techniques for optimization. The work emphasizes the evolution of algorithms from classical REINFORCE to actor-critic methods and trust region approaches like Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO).
Reward: The paper articulates the challenge of sparse reward signals in RL, proposing methodologies such as reward shaping and learning from demonstration to mitigate these issues.
Model: Model-based RL, which constructs representations of the environment to facilitate planning, receives substantial attention. The use of techniques such as Monte Carlo tree search (MCTS) embodies efforts to leverage models within RL frameworks effectively.
Exploration vs. Exploitation: The manuscript presents strategies for balancing exploration and exploitation, a pivotal trade-off in RL. Methods such as epsilon-greedy, intrinsic motivation, and bootstrapped DQNs are discussed as means to enhance exploration efficiency.
Representation: The importance of representation learning is emphasized, given its role in scalable function approximation and policy learning, particularly with the use of deep neural networks. This section covers classical methods and recent innovations in neural architectures tailored for RL.

Important Mechanisms

The paper progresses to discuss six critical mechanisms supporting DRL advancements:

Attention and Memory: Critical for tasks requiring sequential data processing and long-term dependencies, attention mechanisms, and memory networks are underscored for their role in enhancing learning capabilities.
Unsupervised Learning: Mechanisms like unsupervised auxiliary learning and generative adversarial networks (GANs) offer new avenues for leveraging unlabeled data and improving learning efficiency.
Hierarchical and Relational RL: Hierarchical RL approaches tackle tasks involving long horizons and complex dependencies through temporal abstraction, while relational RL incorporates relational reasoning into models, addressing complex data structures.
Multi-Agent Systems: Challenges and methods specific to scenarios involving multiple autonomously learning agents are covered, with emphasis on game-theoretic approaches and centralized vs. decentralized policies.
Learning to Learn: Meta-learning and transfer learning principles are applied to improve the adaptability and efficiency of RL agents across tasks.

Applications

The manuscript concludes with an exploration of diverse applications, emphasizing DRL's practical utility in games, robotics, natural language processing, computer vision, finance, healthcare, and beyond. Each application area demonstrates the adaptability of DRL frameworks to tackle real-world problems, underscoring their potential for transformative impact.

Conclusion

The paper by Yuxi Li encapsulates the maturation of deep reinforcement learning as a field poised for continued innovation and broader applicability. By synthesizing developments across core elements, mechanisms, and applications, it offers a robust guide for further research and practical deployment in various domains. The document serves as both a reference and a forward-looking narrative on the state and future of deep reinforcement learning.

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