Reinforcement Learning (RL) Problem Overview:

• The fundamental idea is to capture key aspects of the real-world problem where a learning agent interacts with its environment to achieve a goal.
• Key elements include:
  • Sensing: The agent must sense the state of the environment.
  • Action: The agent must take actions that affect the state.
  • Goals: The agent must have goals related to the state of the environment.
• These aspects (sensation, action, goal) are presented in their simplest form without oversimplification.

Distinction from Other Learning Paradigms:

• Reinforcement Learning vs. Supervised Learning:
  • Supervised learning involves learning from a training set of labeled examples provided by an external supervisor.
  • The aim of supervised learning is for the system to generalize its responses to act correctly in situations not covered in the training set.
  • While supervised learning is important, it is inadequate for learning through interaction, as it’s impractical to cover all scenarios in advance.
  • RL, in contrast, requires an agent to learn from its own experiences, particularly in uncharted territories where self-learning is crucial.
• Reinforcement Learning vs. Unsupervised Learning:
  • Unsupervised learning typically focuses on finding hidden structures within unlabeled data.
  • Although RL does not rely on examples of correct behavior, it is distinct from unsupervised learning because it focuses on maximizing a reward signal rather than finding hidden structures.
  • While uncovering structures in an agent’s experiences may be beneficial in RL, the primary objective remains maximizing the reward signal.
  • RL is therefore considered a distinct machine learning paradigm alongside supervised and unsupervised learning, with unique goals and methodologies.

Exploration vs. Exploitation Dilemma:

• A key challenge in reinforcement learning (RL) is balancing exploration (trying new actions) and exploitation (using known successful actions).
• To maximize reward, an RL agent must:
  • Exploit: Prefer actions that have previously yielded high rewards.
  • Explore: Try new actions to potentially discover better rewards in the future.
• Neither exploration nor exploitation can be solely relied upon for success; both are necessary.
• On stochastic tasks, actions must be repeated multiple times to obtain a reliable estimate of expected rewards.
• This exploration-exploitation trade-off has been studied extensively and is a unique aspect of RL, not found in supervised or unsupervised learning in their pure forms.

Holistic Approach in RL:

• RL explicitly addresses the entire problem of a goal-directed agent interacting with an uncertain environment.
• This is in contrast to other machine learning approaches that often focus on subproblems without considering how they fit into the larger picture.
• Many current research efforts in machine learning emphasize supervised learning without specifying how generalization abilities would ultimately be useful in real-world applications.
• RL, on the other hand, requires consideration of planning, decision-making in real-time, and the integration of predictive models within the agent’s overall goal-seeking behavior.

Reinforcement Learning’s Holistic Focus:

• RL takes a comprehensive approach, beginning with the complete picture of an interactive, goal-seeking agent.
• RL agents:
  • Have explicit goals.
  • Can sense and influence their environments through actions.
  • Operate under significant uncertainty about their environment.
• Planning in RL requires addressing the interplay between planning and real-time action selection, as well as how environment models are acquired and improved.
• RL incorporates supervised learning for specific tasks but always with a focus on the overall goal of the agent.
• To advance learning research, it is crucial to isolate important subproblems that are integral to the agent’s overall behavior, even if the complete agent’s details are not fully understood yet.
• Addressing the “Curse of Dimensionality”:
• Some reinforcement learning methods can effectively use parameterized approximators to combat the “curse of dimensionality” encountered in operations research and control theory.