Datasets:
jblitzar
/
github-python-corpus

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n_action = action_deque[i][j]
replay_buffer.add(
n_state,
n_action,
next_state[i],
n_reward,
np.uint8(done[i]),
level_seeds[i],
)
expect_new_seed[i] = True
############################################################
state = next_state
for info in infos:
if "episode" in info.keys():
eval_episode_rewards.append(info["episode"]["r"])
if progressbar:
progressbar.update(1)
if record:
for video in eval_envs.get_videos():
wandb.log({"evaluation_behaviour": video})
eval_envs.close()
if progressbar:
progressbar.close()
avg_reward = sum(eval_episode_rewards) / len(eval_episode_rewards)
if print_score:
print("---------------------------------------")
print(f"Evaluation over {num_episodes} episodes: {avg_reward}")
print("---------------------------------------")
############################################################
if args.record_td_error:
with torch.no_grad():
n_batch = 2
loss = 0
for _ in range(n_batch):
_, batch_loss, _ = policy.loss(replay_buffer)
loss += batch_loss.item()
loss /= n_batch * args.batch_size
del replay_buffer
return eval_episode_rewards, loss
############################################################
return eval_episode_rewards
def multi_step_reward(rewards, gamma):
ret = 0.0
for idx, reward in enumerate(rewards):
ret += reward * (gamma ** idx)
return ret
def new_episode(value, estimates, level_seed, i, step):
estimates[level_seed] = value[i].item()
wandb.log(
{f"Start State Value Estimate for Level {level_seed}": value[i].item()},
step=step,
)
def plot_level_returns(level_seeds, returns, estimates, gaps, episode_reward, i, step):
seed = level_seeds[i][0].item()
returns[seed] = episode_reward
gaps[seed] = episode_reward - estimates[seed]
wandb.log({f"Empirical Return for Level {seed}": episode_reward}, step=step)
if __name__ == "__main__":
args = parser.parse_args()
logging.getLogger().setLevel(logging.INFO)
if args.verbose:
logging.getLogger().setLevel(logging.INFO)
else:
logging.disable(logging.CRITICAL)
if args.seed_path:
train_seeds = load_seeds(args.seed_path)
else:
train_seeds = generate_seeds(args.num_train_seeds, args.base_seed)
train(args, train_seeds)
# <FILESEP>
from .categories import NodeCategories
from .core.partial_prompt import PartialPrompt
class RandomPromptScheduleGenerator:
NODE_NAME = "Random Prompt Schedule Generator"
ICON = "🖺"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {