# Vectorized environment with different configurations
**URL:** https://discuss.ray.io/t/vectorized-environment-with-different-configurations/22042
**Category:** Configure Algorithm, Training, Evaluation, Scaling
**Created:** [March 13, 2025, 10:21am UTC](https://discuss.ray.io/t/vectorized-environment-with-different-configurations/22042 "2025-03-13T10:21:07Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@Leonardo\_Lamanna](https://discuss.ray.io/u/Leonardo_Lamanna)
#### Post date: [March 13, 2025, 10:21am UTC](https://discuss.ray.io/t/vectorized-environment-with-different-configurations/22042/1 "2025-03-13T10:21:07Z")
I am trying to learn RLlib for training a PPO agent in a vectorized custom gym environment (named `MNISTExib-v0`) where each environment is instantiated with a different configuration.
I am currently able to train PPO in a single or vectorized environment using the same environment configuration:
```auto
def env_creator(env_config):
return MNISTExib(**env_config)
# Register env
register_env('MNISTExib-v0', env_creator)
# Load a list of environment configurations
with open('env_configs.yaml') as f:
env_configs = yaml.safe_load(f)
# Configure PPO algorithm
config = (
get_trainable_cls('PPO')
.get_default_config()
.environment(
'MNISTExib-v0',
env_config=env_configs[0]
)
.env_runners(
num_env_runners=0,
num_envs_per_env_runner=1,
)
.rl_module(
model_config=DefaultModelConfig(
conv_activation="relu",
head_fcnet_hiddens=[256],
vf_share_layers=True,
conv_filters=[(16, 4, 2), (32, 4, 2)],
)
)
)
# Build PPO agent
agent = config.build_algo()
# Train PPO agent
train_res = agent.train()
```
Ideally, I would like to set `num_envs_per_env_runner=8` and pass a list `env_configs` of size `8` to train in parallel on `8 MNISTExib-v0` environments instantiated with different configurations.
Is this someway possible? Or is there any workaround that does not require changes to `MNISTExib` class?
Thank you!
---
### Author:  [@mannyv](https://discuss.ray.io/u/mannyv)
#### Post date: [March 14, 2025, 1:21pm UTC](https://discuss.ray.io/t/vectorized-environment-with-different-configurations/22042/2 "2025-03-14T13:21:35Z")
Hi @Leonardo_Lamanna,
Check out `EnvDependingOnWorkerAndVectorIndex` in
this section of the documentation.
> **[Environments — Ray 2.43.0](https://docs.ray.io/en/latest/rllib/rllib-env.html#specifying-by-tune-registered-lambda)**
---
### Author:  [@Leonardo\_Lamanna](https://discuss.ray.io/u/Leonardo_Lamanna)
#### Post date: [March 17, 2025, 7:14am UTC](https://discuss.ray.io/t/vectorized-environment-with-different-configurations/22042/3 "2025-03-17T07:14:18Z")
Thank you @mannyv! I would like to train PPO on a training set of environments by splitting them into batches, I will check it out