Reinforcement Learning: An Overview (2412.05265v1)

Abstract: This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement learning and sequential decision making, covering value-based RL, policy-gradient methods, model-based methods, and various other topics (including a very brief discussion of RL+LLMs).

Overview of Reinforcement Learning: An Overview by Kevin P. Murphy

Kevin P. Murphy's paper, "Reinforcement Learning: An Overview," provides an extensive exposition of reinforcement learning (RL), a key methodology in sequential decision-making tasks. The paper dissects a myriad of RL paradigms, models, and algorithms while addressing both foundational elements and advanced topics in RL theory and practice. By this dissemination, the paper serves as a critical resource for researchers intending to delve deeply into the nuances of RL.

Key Concepts and Frameworks

Murphy begins with an examination of sequential decision-making under uncertainty, primarily focusing on the Markov decision process (MDP) framework. The paper defines essential constructs such as the state-value function $V(s)$, action-value function $Q(s,a)$, and optimal policies. These definitions are critical for formalizing how agents can learn to predict future rewards and select actions that maximize cumulative returns.

The overview includes both discrete MDPs and extends to partially observable MDPs (POMDPs), where an agent receives incomplete information about the state space. For these, the concept of belief states is introduced, which requires the agent to maintain probability distributions over the possible states of the environment.

RL Algorithms and Methods

Murphy expounds on three major classes of RL approaches: value-based, policy-based, and model-based RL.

1. Value-Based Methods: Techniques such as Q-learning and Temporal Difference (TD) learning are emphasized for their utility in estimating the value functions from which optimal policies can be derived. The paper touches on recent innovations, like the double Q-learning algorithm, which addresses issues like maximization bias inherent in vanilla Q-learning.
2. Policy-Based Methods: The transition to policy gradient techniques, including REINFORCE and actor-critic methods, is discussed. Unlike value-based methods, these allow direct optimization of the policy and can handle continuous action spaces more naturally. The paper presents Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO), highlighting their advantages in maintaining stable updates and handling large policy networks.
3. Model-Based Methods: Murphy explores model-based RL, where a model of the environment is learned, and planning is used to derive optimal strategies. Techniques such as Model Predictive Control (MPC) and real-time dynamic programming (RTDP) are highlighted for their efficiency in sample usage compared to model-free strategies.

Advanced Topics and Techniques

In addressing more intricate parts of RL, Murphy discusses the exploration-exploitation dilemma, commonly encapsulated by methods like Upper Confidence Bounds (UCB) and Thompson Sampling. These methods enable balanced exploration of state-action spaces while exploiting known rewarding actions efficiently.

The paper also explores RL's synergy with deep learning, as demonstrated in deep Q-networks (DQN) and their extensions such as Dueling DQN and Distributional RL, which utilize neural architectures to approximate complex value functions over high-dimensional state spaces.

Implications and Future Directions

Murphy's discussion underscores the vast potential of RL in real-world applications, from robotics to automated problem-solving agents. However, challenges remain, such as dealing with partial observability, credit assignment in temporal tasks, and scalability in complex environments.

He speculates on future developments, particularly the integration of RL with LLMs, which may offer paradigms where language-driven decision-making frameworks interact seamlessly with environmental data.

Conclusion

In sum, Murphy's proprietary exposition on RL offers a comprehensive survey that bridges fundamental principles with cutting-edge advancements. The paper is a testament to the versatility and depth of reinforcement learning, setting the stage for ongoing innovations and practical implementations in artificial intelligence. For experts in the field, Murphy’s work not only consolidates existing knowledge but also prompts considerations for extending RL methodologies in increasingly complex and dynamic environments.