model.download(f"models/{experiment_name}/")
agent.load_state_dict(
torch.load(f"models/{experiment_name}/agent.pt", map_location=device))
agent.eval()
print(f"resumed at update {starting_update}")
for update in range(starting_update, num_updates + 1):
# 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
# 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():
values[step] = agent.get_value(obs[step]).flatten()
action, logproba, _, invalid_action_masks[step] = agent.get_action(
obs[step], envs=envs)
actions[step] = action
logprobs[step] = logproba
# TRY NOT TO MODIFY: execute the game and log data.
# the real action adds the source units
real_action = torch.cat([
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions.categorical import Categorical
try:
env = MicroRTSGridModeVecEnv(
num_envs=1,
render_theme=2,
ai2s=[microrts_ai.passiveAI],
map_path="maps/16x16/basesWorkersTestAttack16x16.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()
# print("reward is", env.step([[[ 17, 2 , 0 , 3 , 0 , 1 , 2, 0]]])[1])
# env.render()
# print("reward is", env.step([[[ 34 , 4 , 1 ,2 , 1 , 2 , 3 ,0]]])[1])
# env.render()
# print("reward is", env.step([[[ 14*16+14 , 1 , 0 ,0 , 0 , 0 , 0 ,0]]])[1])
# env.render()
# for _ in range(100):
# env.step([[[ 0 , 0 , 0 ,0 , 0 , 0 , 0 ,0]]])
# env.render()
# print("relative target position:", np.where(np.array(env.vec_client.getMasks(0))[0,13,14][1+6+4+4+4+4+7:]==1)[0])
# print("reward is", env.step([[[ 13*16+14 , 5 , 0 ,0 , 0 , 0 , 0 ,23]]])[1])