# Multi Agent Deep Reinforcement Learning for continuous and discrete action

I am looking to have a cooperative multi agent reinforcement learning framework where one agent has a discrete action space and another agent has a continuous action space. Is there a way to do this as most papers I have seen will only handle one or the other.

• you might want to google "parametrized action reinforcement learning" and "parametrized Markov decision process" Sep 10 at 3:24
• Thanks Sanyou seems like what I have been looking for. I just didn't really know the correct terms to google were Sep 10 at 17:49

• I can see how they are handling the parameterized action spaces in env.action_space  where the first space is a Discrete that describes the total number of actions and then the rest are box spaces that hold the high and low value information. However I am not certain how to define my action space in these terms since I have some actions that are just completely discrete. For example I have one action that is just a discrete action between 5 choices and then also 2 other actions that are continuous. All of these actions can be used at a step together. Sep 20 at 22:22