Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination
This work addresses coordination challenges in multi-agent systems, offering an incremental improvement for scenarios requiring dynamic behavior adaptation.
The paper tackles the problem of balancing flexibility and predictability in multi-agent hierarchical reinforcement learning by proposing a dynamic termination Bellman equation, which allows agents to adaptively terminate options and improves performance on pursuit and taxi tasks.
In a multi-agent system, an agent's optimal policy will typically depend on the policies chosen by others. Therefore, a key issue in multi-agent systems research is that of predicting the behaviours of others, and responding promptly to changes in such behaviours. One obvious possibility is for each agent to broadcast their current intention, for example, the currently executed option in a hierarchical reinforcement learning framework. However, this approach results in inflexibility of agents if options have an extended duration and are dynamic. While adjusting the executed option at each step improves flexibility from a single-agent perspective, frequent changes in options can induce inconsistency between an agent's actual behaviour and its broadcast intention. In order to balance flexibility and predictability, we propose a dynamic termination Bellman equation that allows the agents to flexibly terminate their options. We evaluate our model empirically on a set of multi-agent pursuit and taxi tasks, and show that our agents learn to adapt flexibly across scenarios that require different termination behaviours.