def main():
device = torch.device("cpu")
env = UnityEnvironment(file_name='reacher20/reacher', base_port=64739)
# get the default brain
brain_name = env.brain_names[0]
brain = env.brains[brain_name]
# reset the environment
env_info = env.reset(train_mode=True)[brain_name]
action_size = brain.vector_action_space_size
num_agents = len(env_info.agents)
states = env_info.vector_observations
state_size = states.shape[1]
agent = PPOAgent(state_size=state_size,
action_size=action_size,
hidden_size=256,
num_agents=num_agents,
random_seed=0,
ppo_epochs=4,
mini_batch_size=128,
normalize_advantages=True,
learning_rate=3e-4,
clip_gradients=True,
gamma=0.99,
tau=0.95,
device=device)
agent.load_model('assets/ppo_checkpoint_37.10.pth')
test_agent(env, brain_name, agent, device, real_time=True)
def evaluate(args):
env = gym.make(args.env)
env_params = get_env_params(env, args)
env.close()
agent = PPOAgent(args, env_params)
agent.load_model(load_model_remark=args.load_model_remark)
parent_conn, child_conn = Pipe()
worker = AtariEnvironment(args.env,
1,
child_conn,
is_render=True,
max_episode_step=args.max_episode_step)
worker.start()
for i_episode in range(100):
obs = worker.reset()
while True:
obs = np.expand_dims(obs, axis=0)
action = agent.choose_action(obs / 255)
parent_conn.send(action[0])
obs_, r, done, info = parent_conn.recv()
obs = obs_
if done:
break
def main(args):
model_store_sprefix = "snapshot"
# NormalizedEnv
env = gym.make(args.env)
env.seed(args.seed)
torch.manual_seed(args.seed)
env, generator, model, cont = get_functions(env, args)
optimizer = optim.Adam(model.parameters(), lr=args.rllr)
memory = Memory(args)
agent = PPOAgent(args, model, optimizer, env, generator, memory, cont)
if args.resume:
agent.load_model(model_store_sprefix)
agent.train(model_store_sprefix, args.save_interval)
env = gym.make(args.env)
envs = SubprocVecEnv([make_env(args.env) for i in range(args.n_envs)])
n_inputs = envs.observation_space.shape[0]
n_outs = envs.action_space.n
agent = PPOAgent(lr=args.lr,
n_inputs=n_inputs,
n_hidden=args.n_hidden,
n_outs=n_outs,
td_n=args.td_n,
ppo_epochs=args.ppo_epochs,
mini_batch_size=args.mini_batch_size)
if args.load_best_pretrained_model:
agent.load_model('../models/ppo/model.pt')
print('Loaded pretrained model')
if args.test_env:
state = env.reset()
done = False
score = 0
while not done:
env.render()
dist, value = agent.step(state)
action = dist.sample()
state, reward, done, _ = env.step(action.cpu().numpy())
score += reward
print(score)
else:
