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Reinforcement Learning Assignment 3

1 Introduction
The goal of this assignment is to do experiment with model-free control, including on-policy learning (Sarsa) and off-policy learning (Q-learning). For deep
understanding of the principles of these two iterative approaches and the differences between them, you will implement Sarsa and Q-learning at the application
of the Cliff Walking Example, respectively.
2 Cliff Walking

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Reinforcement Learning Assignment 3

1 Introduction
The goal of this assignment is to do experiment with model-free control, including on-policy learning (Sarsa) and off-policy learning (Q-learning). For deep
understanding of the principles of these two iterative approaches and the differences between them, you will implement Sarsa and Q-learning at the application
of the Cliff Walking Example, respectively.
2 Cliff Walking
Figure 1: Cliff Walking
Consider the gridworld shown in the Figure 1. This is a standard undiscounted, episodic task, with start state (S), goal state (G), and the usual actions
causing movement up, down, right, and left. Reward is -1 on all transitions except those into the region marked “The Cliff”. Stepping into this region incurs
a reward of -100 and sends the agent instantly back to the start.
3 Experiment Requirments
• Programming language: python3
• You should build the Cliff Walking environment and search the optimal
travel path by Sara and Q-learning, respectively.
• Different settings for ? can bring different exploration on policy update.
Try several ? (e.g. ? = 0.1 and ? = 0) to investigate their impacts on
performances.
2
4 Report and Submission
• Your reports and source code should be compressed and named after ”studentID+name”.