def eval_policy(policy, env, render=False):
	"""
		The main function to evaluate our policy with. It will iterate a generator object
		"rollout", which will simulate each episode and return the most recent episode's
		length and return. We can then log it right after. And yes, eval_policy will run
		forever until you kill the process. 

		Parameters:
			policy - The trained policy to test, basically another name for our actor model
			env - The environment to test the policy on
			render - Whether we should render our episodes. False by default.

		Return:
			None

		NOTE: To learn more about generators, look at rollout's function description
	"""
	# Rollout with the policy and environment, and log each episode's data
	for ep_num, (ep_len, ep_ret) in enumerate(rollout(policy, env, render)):
		_log_summary(ep_len=ep_len, ep_ret=ep_ret, ep_num=ep_num)
The main function to evaluate our policy with. It will iterate a generator object
"rollout", which will simulate each episode and return the most recent episode's
length and return. We can then log it right after. And yes, eval_policy will run
forever until you kill the process.

Parameters:
policy - The trained policy to test, basically another name for our actor model
env - The environment to test the policy on
render - Whether we should render our episodes. False by default.

Return:
None

NOTE: To learn more about generators, look at rollout's function description
eval_policy.py