What is Multi-Agent Reinforcement Learning (MARL)?
Multi-Agent Reinforcement Learning (MARL) refers to the application of single-agent reinforcement learning in scenarios in which multiple agents can communicate and simultaneously influence the environment. The reward is increased when an agent successfully picks up an object or accomplishes another action. The main challenge that MARL poses is the non-stationarity of the environment from the view of each individual agent as the agents are learning and adapting to each other.
In a formal mathematical sense, MARL can be modeled using a framework called Markov Games or Stochastic Games.
A Markov Game for N agents is defined by:
- A set of states S.
- A set of actions Ai , for each agent i.
- A state transition function P : S x A1 x A2 x A3 x . . . . . . x An → △(S), where △(S)probability distribution over states.
- A reward function Ri : S x A1 x A2 x A3 x . . . . x An → R for each agent i.
The objective for each agent i is to learn a policy [Tex]\pi_i = S \rightarrow A [/Tex]that maximizes its expected cumulative reward [Tex]\Epsilon [\sum_{t=0}^{\infty} \gamma^{T} R_i(s_t, a_{1,t}, a_{2,t}, \dots , a_{N,t})][/Tex].
Here,
- [Tex]\gamma[/Tex] is the discount factor
- [Tex](s_t, a_{1,t}, a_{2,t}, \dots , a_{N,t})[/Tex] denotes the state and actions of all the agents at the time t.
Multi-Agent Reinforcement Learning in AI
Reinforcement learning (RL) can solve complex problems through trial and error, learning from the environment to make optimal decisions. While single-agent reinforcement learning has made remarkable strides, many real-world problems involve multiple agents interacting within the same environment. This is where multi-agent reinforcement learning (MARL) comes into play, offering a framework for agents to learn, collaborate, and compete, thereby enhancing their collective performance.
This article delves into the concepts, challenges, and applications of Multi-Agent Reinforcement Learning (MARL) in AI.