Overview
Dive into Reinforcement Learning basics through relatable examples like dog training, exploring key components such as agents, environment, states, actions, rewards, and policy.
Syllabus
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- Introduction to Reinforcement Learning
-- Definition and Key Concepts
-- Real-world Analogies (e.g., Dog Training)
- Key Components of Reinforcement Learning
-- Agents
-- Environments
-- States
-- Actions
-- Rewards
-- Policies
- Agent-Environment Interaction
-- Understanding the Role of an Agent
-- Defining the Environment
-- State Transitions and State Spaces
- Actions and Decision-Making
-- Action Spaces
-- Exploration vs. Exploitation
- Reward Mechanisms
-- Designing Reward Functions
-- Delayed Rewards and Long-term Goals
- Policy and Policy Functions
-- Greedy Policies
-- Stochastic Policies
- Basic Algorithms in Reinforcement Learning
-- Value-Based Methods (e.g., Q-learning)
-- Policy-Based Methods
- Case Study: Dog Training Analogy
-- Mapping Reinforcement Learning Concepts to Dog Training
-- Examples and Exercises
- Summary and Key Takeaways
-- Review of Core Concepts
-- Discussion on Practical Applications
- Q&A Session
-- Addressing Students’ Questions
-- Clarifications and Further Resources
Taught by
Tags
On-Demand
