agent.load_state_dict(target_agent.state_dict())
break
## CRASH AND RESUME LOGIC:
if args.prod_mode:
if not os.path.exists(f"models/{experiment_name}"):
os.makedirs(f"models/{experiment_name}")
torch.save(agent.state_dict(), f"{wandb.run.dir}/agent.pt")
wandb.save(f"agent.pt")
# TRY NOT TO MODIFY: record rewards for plotting purposes
writer.add_scalar("charts/learning_rate",
optimizer.param_groups[0]['lr'], global_step)
writer.add_scalar("charts/update", update, global_step)
writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
writer.add_scalar("losses/policy_loss", pg_loss.item(),
global_step)
writer.add_scalar("losses/entropy",
entropy.mean().item(), global_step)
writer.add_scalar("losses/approx_kl", approx_kl.item(),
global_step)
if args.kle_stop or args.kle_rollback:
writer.add_scalar("debug/pg_stop_iter", i_epoch_pi,
global_step)
print("SPS:", int(global_step / (time.time() - start_time)))
print(stopwatch.format_report(sw.get_last_aggregated_report()))
envs.close()
writer.close()
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])
if done:
env.reset()
env.close()