Reinforcement Learning and Decision Making
Reinforcement Learning (RL) is a fundamental domain of artificial intelligence where agents learn to make decisions based on feedback from their environment. The chapter details the structure of Markov Decision Processes, explores various RL algorithms including value-based and policy-based methods, and discusses the integration of deep learning in reinforcement training. It further examines the real-world applications and challenges faced in implementing RL systems.
Enroll to start learning
You've not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Reinforcement Learning teaches agents to learn from their actions and rewards.
- Markov Decision Processes form the theoretical basis for decision-making in RL.
- Deep Reinforcement Learning combines traditional RL methodologies with neural network architectures for enhanced performance.
Key Concepts
- -- Reinforcement Learning (RL)
- A type of machine learning where agents learn to make decisions by maximizing cumulative rewards from their interactions with an environment.
- -- Markov Decision Process (MDP)
- A mathematical framework used to describe an environment for reinforcement learning, consisting of states, actions, transition probabilities, rewards, and a discount factor.
- -- ValueBased Methods
- Approaches in RL where the agent learns the value of possible actions to inform decision-making.
- -- PolicyBased Methods
- Techniques in RL that focus on learning a policy that directly maps states to actions rather than learning value functions.
- -- Deep Reinforcement Learning (DRL)
- An integration of deep learning with reinforcement learning techniques, utilizing neural networks to approximate policies or value functions.
- -- Exploration vs. Exploitation
- The dilemma faced in reinforcement learning where an agent must choose between trying new actions (exploration) and optimizing actions based on known rewards (exploitation).
Additional Learning Materials
Supplementary resources to enhance your learning experience.