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)
def test(env, datapath, actor_model, mode):
print(f"Testing {actor_model}", flush=True)
if actor_model == '':
print(f"Didn't specify model file. Exiting.", flush=True)
sys.exit(0)
obs_dim = env.observation_space.shape[0]
act_dim = env.action_space.shape[0]
entries = os.listdir(datapath)
entries = [int(x) for x in entries]
entries.sort()
if mode == 'test':
policy = FeedForwardNN(obs_dim, act_dim)
actor_model = datapath + "/" + str(entries[-1]) + "/" + actor_model
policy.load_state_dict(torch.load(actor_model))
print("Iteration " + str(entries[-1]))
eval_policy(policy=policy, env=env, render=True)
if mode == 'progress':
eval_progress(list_dir = entries, file_dir = datapath, actor_model = actor_model,\
obs_dim = obs_dim, act_dim = act_dim, env = env, render = True)
'''