What are the strategies for computationally heavy environments or long-time waiting environments? I have an environment that is computationally heavy (takes several seconds to get a reward and next state). This limits reinforcement capability, due to poor sampling of the problem. There is any strategy that could be used to address the problem (e.g. If I can use the environment in parallel, then I could use a multi-agent approach)
You could use Ray RLlib. It has support for parallel environments, even over multiple GPUs and compute nodes.