Description

1 Assignment Overview
The goal of the assignment is to help you understand the concept of policy gradient algorithms, to implement the actor-critic algorithm and apply it to solve OpenAI gym environments. We will train our networks on various reinforcement learning (RL) environments.
Part I [Total: 60 points] – Implementing Actor-Critic
In this part we will implement an actor-critic algorithm and test it on any simple environment.
2. Train your implemented algorithm on any environment. Check “Suggested environments” section.
3. Show and discuss your results after applying your actor-critic implementation on the environment. Plots should include the total reward per episode.
4. Provide the evaluation results. Run your environment for at least 10 episodes, where the agent chooses only greedy actions from the learnt policy. Plot should include the total reward per episode.
Part II [Total: 40 points] – Solving Complex Environments
In this part, test your actor-critic algorithm implemented in Part I on any other two complex environments.
1. Choose an environment. At least one of the environments has to be among “Suggested environments Part II”. The environment with multiple versions is considered as one environment.
2. Apply your actor-critic algorithm to solve it. You can adjust the neural network structure or hyperparameters from your base implementation.
3. Show and discuss your results after applying the actor-critic implementation on the environment. Plots should include the total reward per episode.
4. Provide the evaluation results. Run your environment for at least 10 episodes, where the agent chooses only greedy actions from the learnt policy. Plot should include the total reward per episode.
5. Go to Step 1. Provide the results for TWO environments.
Suggested environments
Part I
• Your grid world from A1 or A2,
• OpenAI CartPole
• OpenAI Acrobot
• OpenAI Mountain Car
• OpenAI Pendulum
Part II
• Any multi-agent environment
• OpenAI LunarLander
• OpenAI BipedalWalker
• OpenAI HandManipulateBlock
• OpenAI Ant
• RL Bench (link)
• Any other complex environment that you will use for your Final Project
In your report:
1. Discuss the algorithm you implemented.
2. What is the main difference between the actor-critic and value based approximation algorithms?
3. Briefly describe THREE environments that you used (e.g. possible actions, states, agent, goal, rewards, etc). You can reuse related parts from your Assignment 2 report.
4. Show and discuss your results after training your Actor-Critic agent on each environment. Plots should include the reward per episode for THREE environments. Compare how the same algorithm behaves on different environments while training.
5. Provide the evaluation results for each environments that you used. Run your environments for at least 10 episodes, where the agent chooses only greedy actions from the learnt policy. Plot should include the total reward per episode.
6. If you are working in a team of two people, we expect equal contribution for the assignment. Provide contribution summary by each team member.
Extra Points [max +12 points]
• Implement more complex actor-critic [7 points]
Extend your actor-critic algorithm to a more advanced version, e.g. TRPO/DDPG/TD3/SAC. Compare the results after applying it to the same THREE environments used in the assignment. Provide three rewards dynamic plots for each environment with the results of two algorithms: actor-critic and improved version. Disscuss the results.
• Solve Image-based Environment [5 points]
Use one of the environments with image representation of the state that requires a utilization of CNN (Convolution Neural Network) for the state preprocessing (e.g. OpenAI Breakout).
2 Deliverables
Submit your work using UBLearns group in both cases if you work individually or in a team of two. There are two parts in your submission:
2.1 Report
UBIT_TEAMMATE1_TEAMMATE2_assignment3_report.pdf
(e.g. avereshc_nitinvis_assignment3_report.pdf)
2.2 Code
Python is the only code accepted for this project. Submit the code in Jupyter Notebook with the saved results. You can submit multiple files, but they all need to have a clear name. After executing commands Jupyter Notebook, it should generate all the results and plots that were used in your report and should be able to be printed out in a clear manner. Additionally you can submit the trained parameters, so your results can be fully replicated.
For the final submission, submit your code as Jupyter Notebooks named as
UBIT_TEAMMATE1_TEAMMATE2_assignment3.ipynb
(e.g. avereshc_nitinvis_assignment3.ipynb) or
UBIT_TEAMMATE1_TEAMMATE2_assignment3_partI.ipynb
UBIT_TEAMMATE1_TEAMMATE2_assignment3_partII.ipynb
3 References
• NeurIPS Styles (docx, tex)
• Overleaf (LaTex based online document generator) – a free tool for creating professional reports
• GYM environments
• Lecture slides
4 ASSIGNMENT STEPS
• Register your team at UBLearns (UBlearns > Tools > Groups). In case you joined the wrong group, make a private post on piazza.
• Fully complete all parts of the assignment
“I/we certify that the code and data in this assignment were generated independently, using only the tools and resources defined in the course and that I/we did not receive any external help, coaching or contributions during the production of this work.”
• Add your combined pdf and ipynb for Part 1 and Part 2 to a zip file
TEAMMATE1_TEAMMATE2_assignment3_final.zip
(e.g. avereshc_nitinvis_assignment3_final.zip)
• Submit at UBLearns > Assignments
• The code of your implementations should be written in Python. You can submit multiple files, but they all need to be labeled clearly.
• Your Jupyter notebook should be saved with the results
• Include all the references that have been used to complete the assignment
Team Member Assignment Part Contribution (%)

This assignment can be completed individually or in a team of two students. Teams can not be the same for
IMPORTANT NOTE
“I/we certify that the code and data in this assignment were generated independently, using only the tools and resources defined in the course and that I/we did not receive any external help, coaching or contributions during the production of this work.”
6 Important Information
This assignment can be completed in groups of two or individually. Teams can not be the same for A2 & A3 assignments.
7 Late Days Policy
8 Important Dates