Machine Learning
173 learners
Game On: Integrating RL Agents with Environments
In this course, we integrate the grid-world environment with a Q-learning agent, focusing on agent-environment interaction and training over multiple episodes. We explore the exploration vs. exploitation tradeoff using an ε-greedy strategy and visualize performance through reward plots and policy displays.
Numpy
Python
4 lessons
19 practices
1 hour
Coding and Data Algorithms
Course details
Integrating Agents with Environments in Reinforcement Learning
Agent-Environment Interaction Loop Integration
Evaluating Agent Performance Over Time
Fix the Q-Learning Bug
Visualize Your Agent in Action
Integrate Agent with Environment
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