[question] Why is the environment instantiated differently for DDPG and DQN?

Hello,

Thanks a lot for this amazing code.

I noticed that the environment is instantiated differently when using either DQN or DDPG. Specifically at line 249 of train.py, the env is created with:

env = gym.make(env_id, **env_kwargs)
env.seed(args.seed)

in the case of DQN and DDPG whereas it is created with the make_env helper:

env = DummyVecEnv([make_env(env_id, 0, args.seed, wrapper_class=env_wrapper, log_dir=log_dir, env_kwargs=env_kwargs)])

for all the other algorithms.

This means that the environment is not vectorized, it is not possible to specify the log directory and to monitor the training. Why did you make a special case for DQN and DDPG?

Thanks

[PierreExeter]

I meant: do you have a script somewhere or you wrote the latex file manually?

It’s a template I use for writing report. It’s pretty basic but here it is if that can be of any use.

DQN-DDPG_vec_noVec.zip

[PierreExeter]

FYI,

Vectorizing DQN or DDPG does not seem to affect the training, the average return or the training time significantly, see results attached.

comparison_vec_noVec.pdf