def record_test(directory, video_path, n):
make_Dirs(video_path + n + '/')
env = gym_make(ENV_ID)
env = wrappers.Monitor(env,
video_path + n + '/',
video_callable=lambda episode_id: True,
force=True)
env.seed(int(n) * 7)
np.random.seed(int(n) * 7)
torch.manual_seed(int(n) * 7)
agent = MujocoFfAgent()
agent.initialize(env.spaces)
netword_state_dict = None
try:
network_state_dict = torch.load(directory + 'agent_model.pth')
except (FileNotFoundError):
print("No data found for the PPO agent (No existing model).")
network_state_dict = None
return
if network_state_dict != None:
agent.load_state_dict(network_state_dict)
else:
return
agent.to_device(0)
frame_idx = 0
print("Start Test Episode for {}".format(n))
done = False
### Interaction
step = 0
state = env.reset()
prev_action = env.action_space.sample()
prev_reward = 0.
while not done: # or step < MAX_STEPS:
env.render()
state = torch.FloatTensor(state)
prev_action = torch.FloatTensor(prev_action)
prev_reward = torch.FloatTensor([prev_reward])
#agent.eval_mode(step) # determinitic distribution. The std is ignored.
action = agent.step(state, prev_action, prev_reward).action
action = action.detach().cpu().numpy()
next_state, reward, done, _ = env.step(action)
state = next_state
prev_action = action
prev_reward = reward
frame_idx += 1
step += 1
if done:
break
env.close()
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--path',
help='path to params.pkl',
default='/home/alex/parkour-learning/data/params.pkl')
parser.add_argument(
'--env',
default='HumanoidPrimitivePretraining-v0',
choices=['HumanoidPrimitivePretraining-v0', 'TrackEnv-v0'])
parser.add_argument('--algo', default='ppo', choices=['sac', 'ppo'])
args = parser.parse_args()
snapshot = torch.load(args.path, map_location=torch.device('cpu'))
agent_state_dict = snapshot['agent_state_dict']
env = GymEnvWrapper(gym.make(args.env, render=True))
if args.algo == 'ppo':
if args.env == 'TrackEnv-v0':
agent = MujocoFfAgent(ModelCls=PpoMcpVisionModel)
else:
agent = MujocoFfAgent(ModelCls=PPOMcpModel)
else:
if args.env == 'TrackEnv-v0':
agent = SacAgent(ModelCls=PiVisionModel,
QModelCls=QofMuVisionModel)
else:
agent = SacAgent(ModelCls=PiMCPModel, QModelCls=QofMCPModel)
agent.initialize(env_spaces=env.spaces)
agent.load_state_dict(agent_state_dict)
agent.eval_mode(0)
simulate_policy(env, agent)