How severe does this issue affect your experience of using Ray?
- High: It blocks me to complete my task.
I got a multiple env like this.
envs = ["env1","env2","env3", "env4","env5","env6","env7","env8","env9"] class MultiTaskEnv(gym.Env): def __init__(self, env_config): for i in range(len(envs)): if env_config.worker_index%9==i: self.env = gym.make(envs[i], full_action_space=True) self.name= envs[i]
For example, there are 2 parts in the default model (visionnet), cnn layer and fc layer. When I train the agent on a multienv setup, both cnn layer and fc layer weights are shared across all the different environments. But now I want to have the visionnet model as a backbone that is shared across all the envs, and additional fc layers for each of the different envs (that are not shared across the envs)