def test(env, actor_model):
	"""
		Tests the model.

		Parameters:
			env - the environment to test the policy on
			actor_model - the actor model to load in

		Return:
			None
	"""
	print(f"Testing {actor_model}", flush=True)

	# If the actor model is not specified, then exit
	if actor_model == '':
		print(f"Didn't specify model file. Exiting.", flush=True)
		sys.exit(0)

	# Extract out dimensions of observation and action spaces
	obs_dim = env.observation_space.shape[0]
	act_dim = env.action_space.shape[0]

	# Build our policy the same way we build our actor model in PPO
	policy = FeedForwardNN(obs_dim, act_dim)

	# Load in the actor model saved by the PPO algorithm
	policy.load_state_dict(torch.load(actor_model))

	# Evaluate our policy with a separate module, eval_policy, to demonstrate
	# that once we are done training the model/policy with ppo.py, we no longer need
	# ppo.py since it only contains the training algorithm. The model/policy itself exists
	# independently as a binary file that can be loaded in with torch.
	eval_policy(policy=policy, env=env, render=True)
Tests the model.

Parameters:
env - the environment to test the policy on
actor_model - the actor model to load in

Return:
None
main.py