model.download(f"models/{experiment_name}/")
agent.load_state_dict(torch.load(f"models/{experiment_name}/agent.pt"))
agent.eval()
print(f"resumed at update {starting_update}")
for update in range(starting_update, num_updates + 1):
with sw.timer('main'):
# Annealing the rate if instructed to do so.
if args.anneal_lr:
frac = 1.0 - (update - 1.0) / num_updates
lrnow = lr(frac)
optimizer.param_groups[0]['lr'] = lrnow
with sw.timer('rollouts'):
# TRY NOT TO MODIFY: prepare the execution of the game.
for step in range(0, args.num_steps):
envs.render()
global_step += 1 * args.num_envs
obs[step] = next_obs
dones[step] = next_done
# ALGO LOGIC: put action logic here
with torch.no_grad():
with sw.timer('inference_value'):
values[step] = agent.get_value(obs[step]).flatten()
with sw.timer('inference_action'):
action, logproba, _, invalid_action_masks[
step] = agent.get_action(obs[step],
envs=envs,
sw=sw)
actions[step] = action.T
from gym_microrts import microrts_ai
from gym.envs.registration import register
from gym_microrts import Config
try:
env = MicroRTSVecEnv(num_envs=1,
render_theme=2,
ai2s=[microrts_ai.coacAI],
map_path="maps/16x16/basesWorkers16x16.xml",
reward_weight=np.array(
[10.0, 1.0, 1.0, 0.2, 1.0, 4.0]))
# env = gym.make('MicrortsDefeatCoacAIShaped-v3').env
# env = gym.wrappers.RecordEpisodeStatistics(env)
# env.action_space.seed(0)
obs = env.reset()
env.render()
except Exception as e:
e.printStackTrace()
env.action_space.seed(0)
env.reset()
for i in range(10000):
env.render()
action_mask = np.array(env.vec_client.getUnitLocationMasks()).flatten()
time.sleep(0.001)
action = env.action_space.sample()
# optional: selecting only valid units.
if len(action_mask.nonzero()[0]) != 0:
action[0] = action_mask.nonzero()[0][0]
next_obs, reward, done, info = env.step([action])