<p>You could use Ray RLlib. It has support for parallel environments, even over multiple GPUs and compute nodes.</p>

CC BY-SA 4.0
2020-10-18T16:22:11.060Z
2020-10-18T16:22:11.060Z
Tom Dörr
<p>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)</p>

CC BY-SA 4.0
2020-09-22T18:28:58.923Z
Mltiple environments, installing some form of reward shaping or prior knowledge into the problem?
FourierFlux
CC BY-SA 4.0
2020-09-23T14:53:11.003Z
@FourierFlux
In terms of multiple environments - then how you would integrate them? Average updates like in A3C alg. ?
Daniel Wiczew
<reinforcement-learning><environment>
What are the strategies for computationally heavy environments or long-time waiting environments?
Daniel Wiczew
# What are the strategies for computationally heavy environments or long-time waiting environments?

(Asked by: username_0 on 2020-09-22)

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)

**Tags:** reinforcement-learning, environment

> **Comments:**
> **username_1**: Mltiple environments, installing some form of reward shaping or prior knowledge into the problem?  
> **username_0**: @username_1 In terms of multiple environments - then how you would integrate them? Average updates like in A3C alg. ?  

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## 1 answers

### Answer by username_2 on 2020-10-18. Score: 0

You could use Ray RLlib. It has support for parallel environments, even over multiple GPUs and compute nodes.


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