expected to be in range of [-1, 0], but got 1

[WillInvest]

import argparse
import datetime
import os
import sys
import pprint
import numpy as np
import torch

Add the parent directory to the system path

sys.path.append('..')

from tianshou.data import Collector, ReplayBuffer, VectorReplayBuffer, PrioritizedVectorReplayBuffer, Batch
from tianshou.env.venvs import DummyVectorEnv, SubprocVectorEnv
from tianshou.exploration import GaussianNoise
from tianshou.policy import DDPGPolicy
from tianshou.policy.base import BasePolicy
from tianshou.trainer import OffpolicyTrainer
from tianshou.utils.net.common import Net
from tianshou.utils.net.continuous import Actor, Critic
from tianshou.highlevel.logger import LoggerFactoryDefault

from env.amm_env import ArbitrageEnv
from env.market import GBMPriceSimulator
from env.new_amm import AMM

def get_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--task", type=str, default="AMM")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--alpha", type=float, default=0.5)
parser.add_argument("--beta", type=float, default=0.4)
parser.add_argument("--buffer-size", type=int, default=1e6)
parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[256, 256])
parser.add_argument("--actor-lr", type=float, default=1e-5)
parser.add_argument("--critic-lr", type=float, default=1e-5)
parser.add_argument("--gamma", type=float, default=0.0)
parser.add_argument("--tau", type=float, default=0.0005)
parser.add_argument("--exploration-noise", type=float, default=0.01)
parser.add_argument("--start-timesteps", type=int, default=1)
parser.add_argument("--epoch", type=int, default=200)
parser.add_argument("--step-per-epoch", type=int, default=5000)
parser.add_argument("--step-per-collect", type=int, default=10)
parser.add_argument("--update-per-step", type=int, default=1)
parser.add_argument("--n-step", type=int, default=3)
parser.add_argument("--batch-size", type=int, default=64)
parser.add_argument("--training-num", type=int, default=10)
parser.add_argument("--test-num", type=int, default=10)
parser.add_argument("--logdir", type=str, default="log")
parser.add_argument("--render", type=float, default=0.0)
parser.add_argument(
"--device",
type=str,
default="cuda" if torch.cuda.is_available() else "cpu",
)
parser.add_argument("--resume-path", type=str, default=None)
parser.add_argument("--resume-id", type=str, default=None)
parser.add_argument(
"--logger",
type=str,
default="tensorboard",
choices=["tensorboard", "wandb"],
)
parser.add_argument("--wandb-project", type=str, default="mujoco.benchmark")
parser.add_argument(
"--watch",
default=False,
action="store_true",
help="watch the play of pre-trained policy only",
)
parser.add_argument("--USING_USD", type=bool, default=True)
parser.add_argument("--mkt_start", type=float, default=1.0)
parser.add_argument("--fee_rate", type=float, default=0.02)

return parser.parse_args()

def test_ddpg(args: argparse.Namespace = get_args()) -> None:
market = GBMPriceSimulator(start_price=args.mkt_start, deterministic=False)
fee_rate = args.fee_rate
amm = AMM(initial_a=10000, initial_b=10000, fee=fee_rate)
env = ArbitrageEnv(market, amm, USD=args.USING_USD)
eval_market = GBMPriceSimulator(start_price=args.mkt_start, deterministic=True)
# test_env = ArbitrageEnv(eval_market, amm, USD=args.USING_USD)
train_env = SubprocVectorEnv([lambda: ArbitrageEnv(market, amm, USD=args.USING_USD) for _ in range(args.training_num)])
test_env = SubprocVectorEnv([lambda: ArbitrageEnv(eval_market, amm, USD=args.USING_USD) for _ in range(args.test_num)])

args.state_shape = env.observation_space.shape or env.observation_space.n
args.action_shape = env.action_space.shape or env.action_space.n
args.max_action = env.action_space.high[0]
args.exploration_noise = args.exploration_noise * args.max_action
args.USING_USD = False
print("Observations shape:", args.state_shape)
print("Actions shape:", args.action_shape)
print("Action range:", np.min(env.action_space.low), np.max(env.action_space.high))
print(f"max_action: {args.max_action}")

# seed
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# model
net_a = Net(state_shape=args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
actor = Actor(net_a, args.action_shape, max_action=args.max_action, device=args.device).to(
    args.device,
)
actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
net_c = Net(
    state_shape=args.state_shape,
    action_shape=args.action_shape,
    hidden_sizes=args.hidden_sizes,
    concat=True,
    device=args.device,
)
critic = Critic(net_c, device=args.device).to(args.device)
critic_optim = torch.optim.Adam(critic.parameters(), lr=args.critic_lr)
policy: DDPGPolicy = DDPGPolicy(
    actor=actor,
    actor_optim=actor_optim,
    critic=critic,
    critic_optim=critic_optim,
    tau=args.tau,
    gamma=args.gamma,
    exploration_noise=GaussianNoise(sigma=args.exploration_noise),
    estimation_step=args.n_step,
    action_space=env.action_space,
)
# load a previous policy
if args.resume_path:
    policy.load_state_dict(torch.load(args.resume_path, map_location=args.device))
    print("Loaded agent from: ", args.resume_path)

# collector
buffer = PrioritizedVectorReplayBuffer(
    args.buffer_size,
    buffer_num=len(train_env),
    ignore_obs_next=True,
    save_only_last_obs=True,
    alpha=args.alpha,
    beta=args.beta,
)
train_collector = Collector(policy, train_env, buffer, exploration_noise=True)
test_collector = Collector(policy, test_env, exploration_noise=True)
train_collector.reset()
train_collector.collect(n_step=args.start_timesteps, random=True)

# log
now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
args.algo_name = "ddpg"
log_name = os.path.join(args.task, args.algo_name, str(args.seed), now)
log_path = os.path.join(args.logdir, log_name)

# logger
logger_factory = LoggerFactoryDefault()
if args.logger == "wandb":
    logger_factory.logger_type = "wandb"
    logger_factory.wandb_project = args.wandb_project
else:
    logger_factory.logger_type = "tensorboard"

logger = logger_factory.create_logger(
    log_dir=log_path,
    experiment_name=log_name,
    run_id=args.resume_id,
    config_dict=vars(args),
)

def save_best_fn(policy: BasePolicy) -> None:
    torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))
    
def save_dist_fn(policy: BasePolicy) -> None:
    torch.save(policy.state_dict(), os.path.join(log_path, "best_dist_policy.pth"))

if not args.watch:
    # trainer
    result = OffpolicyTrainer(
        policy=policy,
        train_collector=train_collector,
        test_collector=test_collector,
        max_epoch=args.epoch,
        step_per_epoch=args.step_per_epoch,
        step_per_collect=args.step_per_collect,
        episode_per_test=args.test_num,
        batch_size=args.batch_size,
        save_best_fn=save_best_fn,
        save_dist_fn=save_dist_fn,
        logger=logger,
        update_per_step=args.update_per_step,
        test_in_train=False,
        verbose=True,
    ).run()
    pprint.pprint(result)

# Let's watch its performance!

test_env.seed(args.seed)
test_collector.reset()
collector_stats = test_collector.collect(n_episode=args.test_num, render=args.render)
print(collector_stats)

if name == "main":
test_ddpg()

Observations shape: (2,)
Actions shape: (1,)
Action range: -0.99999 0.99999
max_action: 0.9999899864196777
/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/tianshou/policy/modelfree/ddpg.py:93: UserWarning: action_scaling and action_bound_method are only intended to dealwith unbounded model action space, but find actor model boundaction space with max_action=0.9999899864196777.Consider using unbounded=True option of the actor model,or set action_scaling to False and action_bound_method to None.
warnings.warn(
/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/tianshou/data/collector.py:331: UserWarning: n_step=1 is not a multiple of (self.env_num=10), which may cause extra transitions being collected into the buffer.
warnings.warn(
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([4, 2])
obs: torch.Size([4, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([6, 2])
obs: torch.Size([6, 256])
Epoch #1: 0%| | 0/5000 [00:00<?, ?it/s]obs: torch.Size([10, 2])
obs: torch.Size([10, 256])
obs: torch.Size([64])
Epoch #1: 0%| | 0/5000 [00:00<?, ?it/s]
Traceback (most recent call last):
File "/home/shiftpub/AMM-Python/exp/run.py", line 201, in
test_ddpg()
File "/home/shiftpub/AMM-Python/exp/run.py", line 190, in test_ddpg
).run()
^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/tianshou/trainer/base.py", line 590, in run
deque(self, maxlen=0) # feed the entire iterator into a zero-length deque
^^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/tianshou/trainer/base.py", line 322, in next
train_stat, update_stat, self.stop_fn_flag = self.training_step()
^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/tianshou/trainer/base.py", line 461, in training_step
training_stats = self.policy_update_fn(collect_stats)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/tianshou/trainer/base.py", line 671, in policy_update_fn
update_stat = self._sample_and_update(self.train_collector.buffer)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/tianshou/trainer/base.py", line 613, in _sample_and_update
update_stat = self.policy.update(sample_size=self.batch_size, buffer=buffer)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/tianshou/policy/base.py", line 543, in update
batch = self.process_fn(batch, buffer, indices)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/tianshou/policy/modelfree/ddpg.py", line 149, in process_fn
return self.compute_nstep_return(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/tianshou/policy/base.py", line 672, in compute_nstep_return
target_q_torch = target_q_fn(buffer, terminal) # (bsz, ?)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/tianshou/policy/modelfree/ddpg.py", line 141, in _target_q
return self.critic_old(obs_next_batch.obs, self(obs_next_batch, model="actor_old").act)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/tianshou/policy/modelfree/ddpg.py", line 178, in forward
actions, hidden = model(batch.obs, state=state, info=batch.info)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/tianshou/utils/net/continuous.py", line 84, in forward
action_BA, hidden_BH = self.preprocess(obs, state)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/tianshou/utils/net/common.py", line 277, in forward
logits = self.model(obs)
^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/shiftpub/miniconda/envs/amm_tianshou/lib/python3.11/site-packages/tianshou/utils/net/common.py", line 143, in forward
obs = obs.flatten(1)
^^^^^^^^^^^^^^
IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)

[WillInvest]

I noticed that if I change batch size to 32, then the weird "obs: torch.Size([64])" becomes "obs: torch.Size([32])"

so the error is somehow connected to the batch size

[WillInvest]

buffer = PrioritizedVectorReplayBuffer(
    args.buffer_size,
    buffer_num=len(train_env),
    # ignore_obs_next=True,
    # save_only_last_obs=True,
    alpha=args.alpha,
    beta=args.beta,
)

when I comment out those two lines, problem solved.

Can someone help me explain what happens here? Really appreciate.

[dantp-ai]

@WillInvest I can look into it, but can you format your posts above to make the code more readable?