	def _init_hyperparameters(self, hyperparameters):
		"""
			Initialize default and custom values for hyperparameters

			Parameters:
				hyperparameters - the extra arguments included when creating the PPO model, should only include
									hyperparameters defined below with custom values.

			Return:
				None
		"""
		# Initialize default values for hyperparameters
		# Algorithm hyperparameters
		self.timesteps_per_batch = 4800                 # Number of timesteps to run per batch
		self.max_timesteps_per_episode = 1600           # Max number of timesteps per episode
		self.n_updates_per_iteration = 5                # Number of times to update actor/critic per iteration
		self.lr = 0.005                                 # Learning rate of actor optimizer
		self.gamma = 0.95                               # Discount factor to be applied when calculating Rewards-To-Go
		self.clip = 0.2                                 # Recommended 0.2, helps define the threshold to clip the ratio during SGA

		# Miscellaneous parameters
		self.render = False                             # If we should render during rollout
		self.save_freq = 10                             # How often we save in number of iterations
		self.deterministic = False                      # If we're testing, don't sample actions
		self.seed = None								# Sets the seed of our program, used for reproducibility of results

		# Change any default values to custom values for specified hyperparameters
		for param, val in hyperparameters.items():
			exec('self.' + param + ' = ' + str(val))

		# Sets the seed if specified
		if self.seed != None:
			# Check if our seed is valid first
			assert(type(self.seed) == int)

			# Set the seed 
			torch.manual_seed(self.seed)
			print(f"Successfully set seed to {self.seed}")
Initialize default and custom values for hyperparameters

Parameters:
hyperparameters - the extra arguments included when creating the PPO model, should only include
hyperparameters defined below with custom values.

Return:
None
graph_code/ppo_for_beginners/ppo.py