Datasets:
jblitzar
/
github-python

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return [base_seed + i for i in range(num_seeds)]
def load_seeds(seed_path):
seed_path = os.path.expandvars(os.path.expanduser(seed_path))
seeds = open(seed_path).readlines()
return [int(s) for s in seeds]
def eval_policy(
args,
policy,
num_episodes,
num_processes=1,
deterministic=False,
start_level=0,
num_levels=0,
seeds=None,
level_sampler=None,
progressbar=None,
record=False,
print_score=True,
advanced_test=False,
exploration_coeff=1.0
):
if level_sampler:
start_level = level_sampler.seed_range()[0]
num_levels = 1
eval_envs, level_sampler = make_dqn_lr_venv(
num_envs=num_processes,
env_name=args.env_name,
seeds=seeds,
device=args.device,
num_levels=num_levels,
start_level=start_level,
no_ret_normalization=args.no_ret_normalization,
distribution_mode=args.distribution_mode,
paint_vel_info=args.paint_vel_info,
level_sampler=level_sampler,
record_runs=record,
attach_task_id=args.attach_task_id
)
############################################################
if args.record_td_error:
replay_buffer = make_buffer(args, eval_envs)
level_seeds = torch.zeros(args.num_processes)
state_deque: List[deque] = [deque(maxlen=args.multi_step) for _ in range(args.num_processes)]
reward_deque: List[deque] = [deque(maxlen=args.multi_step) for _ in range(args.num_processes)]
action_deque: List[deque] = [deque(maxlen=args.multi_step) for _ in range(args.num_processes)]
expect_new_seed: List[bool] = [False for _ in range(args.num_processes)]
############################################################
eval_episode_rewards: List[float] = []
if level_sampler:
state, _ = eval_envs.reset()
else:
state = eval_envs.reset()
while len(eval_episode_rewards) < num_episodes:
if advanced_test:
action = policy.sample_action(state, c=exploration_coeff)
elif not deterministic and np.random.uniform() < args.eval_eps:
action = (
torch.LongTensor([eval_envs.action_space.sample() for _ in range(num_processes)])
.reshape(-1, 1)
.to(args.device)
)
# if not deterministic:
# action, _ = policy.sample_action(state, temperature=0.05)
else:
with torch.no_grad():
action, _ = policy.select_action(state, eval=True)
next_state, reward, done, infos = eval_envs.step(action)
############################################################
if args.record_td_error:
for i, info in enumerate(infos):
if "bad_transition" in info.keys():
print("Bad transition")
state_deque[i].append(state[i])
reward_deque[i].append(reward[i])
action_deque[i].append(action[i])
if len(state_deque[i]) == args.multi_step or done[i]:
temp_reward = reward_deque[i]
# if args.reward_clip > 0:
# temp_reward = np.clip(temp_reward, -args.reward_clip, args.reward_clip)
n_reward = multi_step_reward(temp_reward, args.gamma)
n_state = state_deque[i][0]
n_action = action_deque[i][0]
replay_buffer.add(
n_state,
n_action,
next_state[i],
n_reward,
np.uint8(done[i]),
level_seeds[i],
)
if done[i]:
reward_deque_i = list(reward_deque[i])
for j in range(1, len(reward_deque_i)):
n_reward = multi_step_reward(reward_deque_i[j:], args.gamma)
n_state = state_deque[i][j]