<p>I also have been trying to do something similar for my problem. You can check out the following papers:</p>
<ol>
<li><a href="http://www.lamda.nju.edu.cn/wanghan/pricai16.pdf" rel="nofollow noreferrer">Exploring Multi-Action Relationship in Reinforcement Learning</a></li>
<li><a href="https://arxiv.org/pdf/1803.05402.pdf" rel="nofollow noreferrer">Imitation Learning with Concurrent Actions in 3D Games</a></li>
<li><a href="https://arxiv.org/pdf/1711.08946.pdf" rel="nofollow noreferrer">Action Branching Architectures for Deep Reinforcement Learning</a></li>
<li><a href="https://arxiv.org/pdf/1708.04782.pdf" rel="nofollow noreferrer">StarCraft II: A New Challenge for Reinforcement Learning</a></li>
</ol>
How to handle simultaneous actions in Reinforcement Learning
<p>I’m trying to use Reinforcement Learning to solve a problem that involves a ton of simultaneous actions. For example, the agent will be able to take actions that can result in a single action, like shooting, or that can result in multiple actions, like shooting while jumping while turning right while doing a karate chop, etc. When all the possible actions are combined, I end up with a huge action array, say 1 x 2000. So my LSTM network output array will have that size. Of course I’ll use a dictionary to decode the action array to apply the actions(s). So my questions are, is that action array too large? Is this the way to handle simultaneous actions? Is there any other way to do this? Feel free to link any concrete examples you have seen around. Thanks.</p>