wrapper problem under simple_speaker_listener environment #120
Description
when using simple_speaker_listener environment, the observation dimension of speaker and lisener is not the same, so there is a problem when wrapper the env by env_wrappers.py
execute this shell
`#!/bin/sh
env="MPE"
scenario="simple_speaker_listener"
#"simple_speaker_listener" "simple_spread"
num_landmarks=3
num_agents=2
algo="rmaddpg"
exp="debug"
seed_max=1
echo "env is ${env}, scenario is ${scenario}, algo is ${algo}, exp is ${exp}, max seed is ${seed_max}"
for seed in
echo "seed is ${seed}:"
CUDA_VISIBLE_DEVICES=0 python3 train/train_mpe.py --env_name ${env} --n_rollout_threads 1 --algorithm_name ${algo} --experiment_name ${exp} --scenario_name ${scenario} --num_agents ${num_agents} --num_landmarks ${num_landmarks} --seed ${seed} --episode_length 25 --actor_train_interval_step 1 --tau 0.005 --lr 7e-4 --num_env_steps 10000000 --use_reward_normalization --share_policy
echo "training is done!"
done
`
output
warm up... Traceback (most recent call last): File "/project/off-policy-release/offpolicy/scripts/train/train_mpe.py", line 193, in <module> main(sys.argv[1:]) File "/project/off-policy-release/offpolicy/scripts/train/train_mpe.py", line 176, in main runner = Runner(config=config) File "/project/off-policy-release/offpolicy/runner/rnn/mpe_runner.py", line 15, in __init__ self.warmup(num_warmup_episodes) File "/project/off-policy-release/offpolicy/runner/rnn/base_runner.py", line 221, in warmup env_info = self.collecter(explore=True, training_episode=False, warmup=True) File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, **kwargs) File "/project/off-policy-release/offpolicy/runner/rnn/mpe_runner.py", line 145, in separated_collect_rollout obs = env.reset() File "/project/off-policy-release/offpolicy/envs/env_wrappers.py", line 439, in reset return np.array(obs) ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (1, 2) + inhomogeneous part. training is done!