def main():
env = gym.make("LunarLander-v2")
agent = Agent(env)
agent.load_state_dict(torch.load("./models/agent.pt"))
agent.eval()
obs = env.reset()
done = False
for i in range(10000):
env.render()
obs = torch.from_numpy(obs).float()
action, _, _ = agent.get_action(obs)
obs, rew, done, info = env.step(action.cpu().numpy())
sleep(0.001)
if done:
obs = env.reset()
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.T
logprobs[step] = logproba
# TRY NOT TO MODIFY: execute the game and log data.
next_obs, rs, ds, infos = envs.step(action.T)
rewards[step], next_done = rs.view(-1), torch.Tensor(ds).to(device)
wins[step, :] = 0
for idx, info in enumerate(infos):
if 'episode' in info.keys():
print(
f"global_step={global_step}, episode_reward={info['episode']['r']}"
)
writer.add_scalar("charts/episode_reward",
info['episode']['r'], global_step)
# 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):
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, _ = agent.get_action(obs[step])
actions[step] = action
logprobs[step] = logproba
# TRY NOT TO MODIFY: execute the game and log data.
next_obs, rs, ds, infos = envs.step(action)
#envs.render()
rewards[step], next_done = rs.view(-1), torch.Tensor(ds).to(device)
for info in infos:
if 'episode' in info.keys():
print(
f"global_step={global_step}, episode_reward={info['episode']['r']}"
)
writer.add_scalar("charts/episode_reward",
agent.load_state_dict(torch.load(args.agent_model_path))
agent.eval()
for update in range(1, num_updates + 1):
# 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()
# raise
action, logproba, _, invalid_action_masks[step] = agent.get_action(
obs[step], envs=envs)
# print(action.T.cpu()[0].numpy(), invalid_action_masks[step].cpu()[0].numpy().sum())
actions[step] = action.T
logprobs[step] = logproba
# TRY NOT TO MODIFY: execute the game and log data.
try:
next_obs, rs, ds, infos = envs.step(action.T)
except Exception as e:
e.printStackTrace()
raise
rewards[step], next_done = rs.view(-1), torch.Tensor(ds).to(device)
for idx, info in enumerate(infos):
if 'episode' in info.keys():