# BYU CS classes

### Site Tools

cs501r_f2018:lab9

### Objective:

• To implement the Proximal Policy Optimization algorithm, and learn about the use of deep learning in the context of deep RL.

### Deliverable:

For this lab, you will turn in a colab notebook that implements the proximal policy optimization (PPO) algorithm. You must provide tangible proof that your algorithm is working.

• 45% Proper design, creation and debugging of an actor and critic networks
• 25% Proper implementation of the PPO loss function and objective on cart-pole (“CartPole-v0”)
• 20% Implementation and demonstrated learning of PPO on another domain of your choice (except VizDoom)
• 10% Visualization of policy return as a function of training

### Description:

For this lab, you will implement the PPO algorithm, and train it on a few simple worlds from the OpenAI gym test suite of problems.

You may use any code you want from the internet to help you understand how to implement this, but all final code must be your own.

To successfully complete this lab, you must do the following:

• Implement a policy network. This should be a mapping from states to probabilities over actions.
• Implement a value network. This should be a mapping from states to the value of that state.
• Implement a loss function for the value network. This should compare the estimated value to the observed return.
• Implement the PPO loss function.
• Train the value and policy networks. This will at least involve:
1. Generating data by generating roll-outs (according to the current policy)
2. Calculating the actual discounted sum of rewards for each rollout
3. Using the value network to estimate the value of each rollout
4. Using actual and estimated value to calculate the advantage

Part of this lab involves a demonstration that your implementation is working properly. Most likely, this will be a measure of return / reward / value over time; as the policy improves, we should see it increase.

Some domains have other, more natural measures of performance. For example, for the cart-pole problem, we can measure how many iterations it takes before the pole falls over and we reach a terminal state; as the policy improves, this number improves.

You are also welcome to be creative in your illustrations - you could use a video of the agent doing something reasonable, for example.

### Hints and helps:

Update: Here is our our lab's implementation of PPO. NOTE: because this code comes with a complete implementation of running on VizDoom, you may not use that as your additional test domain.

Here are some instructions for installing vizdoom on colab.

Here is some code from our reference implementation. Hopefully it will serve as a good outline of what you need to do.

import gym
import torch
...

class PolicyNetwork(nn.Module):
...

class ValueNetwork(nn.Module):
....

def __init__(self, experience):
self._exp = experience
self._num_runs = len(experience)
self._length = reduce(lambda acc, x: acc + len(x), experience, 0)

def __getitem__(self, index):
idx = 0
seen_data = 0
current_exp = self._exp[0]
while seen_data + len(current_exp) - 1 < index:
seen_data += len(current_exp)
idx += 1
current_exp = self._exp[idx]
chosen_exp = current_exp[index - seen_data]
return chosen_exp[0], chosen_exp[4]

def __len__(self):
return self._length

class PolicyDataset(Dataset):
def __init__(self, experience):
super(PolicyDataset, self).__init__()
self._exp = experience
self._num_runs = len(experience)
self._length = reduce(lambda acc, x: acc + len(x), experience, 0)

def __getitem__(self, index):
idx = 0
seen_data = 0
current_exp = self._exp[0]
while seen_data + len(current_exp) - 1 < index:
seen_data += len(current_exp)
idx += 1
current_exp = self._exp[idx]
chosen_exp = current_exp[index - seen_data]
return chosen_exp

def __len__(self):
return self._length

def main():

env = gym.make('CartPole-v0')
policy = PolicyNetwork(4, 2)
value = ValueNetwork(4)

# ... more stuff here...

# Hyperparameters
epochs = 1000
env_samples = 100
episode_length = 200
gamma = 0.9
value_epochs = 2
policy_epochs = 5
batch_size = 32
policy_batch_size = 256
epsilon = 0.2

for _ in range(epochs):
# generate rollouts
rollouts = []
for _ in range(env_samples):
# don't forget to reset the environment at the beginning of each episode!
# rollout for a certain number of steps!

print('avg standing time:', standing_len / env_samples)
calculate_returns(rollouts, gamma)

# Approximate the value function
# train policy network