A reinforcement learning system consists of at least three key components: an agent, an environment, and a policy. The agent is responsible for perceiving the state of the environment, selecting actions to take based on the observed state, and receiving feedback in the form of rewards or penalties. The environment represents the external world in which the agent operates, and it responds dynamically to the actions taken by the agent. The policy guides the agent's decision-making process by mapping observed states to appropriate actions.
The underlying principle of RL is to enable agents to learn from experience rather than relying on explicit programming. This enables them to adapt and discover effective strategies in complex and uncertain environments. RL algorithms employ a trial-and-error approach, where agents explore the environment, receive feedback, and update their policies based on observed rewards. Over multiple iterations, the agent gradually improves its decision-making capabilities, gaining insights into the structure and dynamics of the environment.
The applications of RL span various domains, including robotics, game playing, resource allocation, and control systems. RL has shown remarkable success in mastering challenging tasks such as playing complex games (e.g., Go, chess, and Atari games), controlling autonomous vehicles, optimizing financial portfolios, and more. Its versatility and potential have led to widespread interest and research efforts across academia and industry.
This chapter has the following learning objectives:
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Understanding the basic concepts of single-agent RL.
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Understanding the basic framework of single-node RL systems with several examples.
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Understanding the basic framework of distributed RL systems with several examples.
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Understanding the basic concepts and algorithms of multi-agent RL.
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Understanding the basic framework and challenges of multi-agent RL systems.