Questions tagged [multi-agent-systems]
For questions related to multi-agent systems (MAS), which are systems that involve multiple agents (each of them can have different skills) that cooperate with each other and interact with the environment. There are several challenges faced by MAS, including coordination between agents, security, and task allocation. Multi-agent systems have been applied in areas such as computer science, civil engineering, and electrical engineering.
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Error: Action input is a invalid key
Description:
I am encountering a persistent issue where the model (Llama 3 8B running on Groq) is unable to handle JSON strings passed as arguments. Additionally, on a broader scale, Crew AI systems ...
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Understanding the Data Flow in CREW AI and Clarifying Some Doubts
Context
I'm developing a research agent using crew-ai that generates papers from prompts.
Issue
I'm unsure whether to pass the user's question through tasks or agents. Here's the relevant code:
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Free platforms for multi-agent reinforcement learning algorithms?
I am looking for free platforms which include multi-agent reinforcement learning algorithms such as Q-RTS, MAPPO, QMIX and FACMAC. Any suggestion?
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Training agents to avoid collision with nearby agents and obstacles
I would like to create an agent-based simulation model in which a single agent, a person generated at the space's boundary, aims to reach a predefined destination while avoiding nearby agents and ...
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Q learning (DQN) strategy for a multiplayer zero-sum game
I have been looking for ways to train a Q-learning agent for a multiplayer zero-sum game (a variation of Tic-Tac-Toe in my case). I came up with a learning strategy I haven't found anywhere else, and ...
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Use your own simulation to train a reinforcement learning multi-agent
I am wanting to train an RL multi-agent model to run in a propietary simulation, which is written in C++. Is there a way to change the simulation itself to create an agent, or must I use a ...
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How to correctly train policies in multi-agent RL?
I am diving into Multi-Agent Reinforcement Learning and after reading some literature, I would like to clarify some approaches because I am not quite sure. Now for the following two cases it is clear ...
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What is the relation between non-stationarity and "a moving target problem" in multi-agent reinforcement learning?
If single-agent RL algorithms such as Q-learning is applied to multi-agent systems (e.g. Markov games), the environment from the perspective of the agent is non-stationary, and the agent is faced with ...
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Has anyone here tried to implement MADDPG for a different environment and succeeded?
Has anyone tried implementing the multi-agent RL algorithm MADDPG (I've linked the paper below)? The paper seems to have a good amount citations, and they do have their code on github. However, a few ...
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How can rewards and loss calculation be extended to multiple agents in a vanilla policy gradient RL setting?
Say I have a simple multi-agent reinforcement learning problem using vanilla policy gradient methods (i.e. REINFORCE) that is currently running with one network per agent. If I can say that each of my ...
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Are there assumptions made about Self-Play that don't hold up in regular MA competition?
I read about this paper Efficient Competitive Self-Play Policy Optimization which proposes an algorithm for training a population of agents with self-play using a perturbation based matchmaking ...
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Book/course recommendation on game theory application to multi-agent system (reinforcement learning)
Is there any great game theory book or course that discusses the application of game theory to modern reinforcement learning or multi-agent systems? Or a classic reference book that can help me get a ...
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Can a Reinforcement Learning problem with multiple simultaneous actions be formalized as a Multiagent Partially Observable Markov Decision Process?
Consider the following decision making problem. We have a controller that selects locations from a grid of coordinates and captures an image (observation $o_t$) with a camera at each location (action $...
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How to parallelize multi-agent DDPG (MADDPG)
I am experimenting with MADDPG algorithm implemented in this repo. Since there were only a few agents (2-3) in the implementation (also in the original paper) steps like parameter updates, action ...
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How to implement a rule-based decision maker for an agent-based model?
I had no idea that there is a stack exchange community for A.I. :-/ So I repost this question here in hope of some guidelines. I tried to delve into the materials discussed in AI: A Modern Approach ...
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RLLib - What exactly do the avail_action and action_embed_size represent? How do they work with the action_mask to phase out invalid actions?
So, I'm fairly new to reinforcement learning and I needed some help/explanations as to what the action_mask and avail_action fields alongside the action_embed_size actually mean in RLlib (the ...
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RLlib's Multi-agent PPO continuous actions turn into nan
After some amount of training on a custom Multi-agent sparse-reward environment using RLlib's (1.4.0) PPO network, I found that my continuous actions turn into nan (explodes?) which is probably caused ...
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Policy Gradient ( Advantage actor-critic) for multiple simultaneous continuous actions
i'm trying to solve a problem in which i need to carry out reinforcement learning with multiple simultaneous actions in continuous action space . i checked the multiagent structure; however, im trying ...
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Why is the number of examined nodes $ O(b^{3d/4})$ in $\alpha$-$\beta$ pruning?
I'm taking a course 'Introduction to AI' and, in one of the tutorials, it was written that when pruning the game tree using $\alpha$-$\beta$ boundaries, the number of nodes that will be developed, ...
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Why does Alpha Zero's Neural Network flip the board to be oriented towards the current player?
While reading the AlphaZero paper in preparation to code my own RL algorithm to play Chess decently well, I saw that the
"The board is oriented to the perspective of the current player."
I ...
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How does the Alpha Zero's move encoding work?
I am a beginner in AI. I'm trying to train a multi-agent RL algorithm to play chess. One issue that I ran into was representing the action space (legal moves/or honestly just moves in general) ...
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How would I design a finite budget, cascaded multi agent deep reinforcement learning model?
In most of the multi-agent reinforcement learning models I've found, it seems to generate the observations for each of the agents simultaneously and then uses a centralized critic to assess all of the ...
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What place do Agent Communications Language have in Multi-Agent Systems nowadays?
I am currently working on implementing a Multi-Agent System for Smart Grids.
There's a lot of literature for that and some things confuse me. I have read that there is FIPA, which aimed to create a ...
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Is there multi-agent reinforcement learning model in which (some of the) reward is given by other agent and not by the external environment?
The traditional setting of multiagent reinforcement learning (MARL) is the mode in which there is set of agents and external environment. And the reward is given to each agent - individually or ...
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Designing Policy-Network for Deep-RL with Large, Variable Action Space
I am attempting a project involving training an agent to play a game using deep reinforcement learning.
This project has a few features that complicate the design of the neural network:
The action ...
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330
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How to train the NN of simple agents given a reward system?
I'm not an expert in AI or NN, I gathered most of the information I have from the internet, and I'm looking for advice and guidance.
I'm trying to design a NN that is going to be used by all the ...
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How to handle a changing in the Reinforcement Learning environment where there is increasing or decreasing in number of agents?
I'm working in A2C and I have an environment where there is increasing or decreasing in the number of agents. The action space in the environment will not change but the state will change when new ...
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Can AlphaZero considered as Multi-Agent Deep Reinforcement Learning?
Can AlphaZero considered as Multi-Agent Deep Reinforcement Learning?
I could not find a clear answer on this. I would say yes it is Multi Agent Learning, as there are two Agents playing against each ...
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What is the relation between multi-agent learning and reinforcement learning?
What is the relation between multi-agent learning and reinforcement learning?
Is one a sub-field of the other? For instance, would it make sense to state that your research interest are multi-agent ...
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How does heuristic work with multiple agents?
I have a question for heuristic search with multiple agents. I know how heuristic search works with one agent (ex. one Pacman) but I don't really understand it with multiple agents. Let's say we have ...
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Shouldn't the utility function of two-player zero-sum games be in the range $[-1, 1]$?
In Appendix B of MuZero, they say
In two-player zero-sum games the value functions are assumed to be bounded within the $[0, 1]$ interval.
I'm confused about the boundary: Shouldn't the value/...
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Are there any board game appropriate to examine the performance of multiple agents that cooperate both inter-group and intra-group?
I want to find out scenarios that useful to examine the performance of intra-group and inter-group cooperation in MARL.
Specifically, I prefer a board game (like sudoku) that is suitable for the ...
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Do I need to maintain a separate population in each distributed environment when implementing PBT in a MARL context?
I have questions regarding on how to implement PBT as described in Algorithm 1 (on page 5) in the paper, Population Based Training of Neural Networks to train agents in a MARL (multi-agent ...
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Agents meeting in a directed connected graph
We have a directed connected graph, where nodes are places one can go to and edges are the "roads" between places. We have K agents whose goal is to meet in one node. Agents start in ...
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Formal proof that every purely reactive agent has behaviorally equivalent standard agent
It kind of makes sense intuitively but I'm not sure about a formal proof. I'll start with briefly listing definitions from Intro to Multiagent systems, Wooldridge, 2002 and then give you my reasoning ...
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What are the examples of agents that is represent these characteristics?
I'm looking for examples of AI systems or agents that best represent these five characteristics (one example for each characteristics):
Reactivity
Proactivity
Adaptability
Sociability
Autonomy
It ...
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How would one implement a multi-agent environment with asynchronous action and rewards per agent?
In a single agent environment, the agent takes an action, then observes the next state and reward:
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What is the difference between multi-agent and multi-modal systems?
The Wikipedia definitions are as follows
Multi-agent systems - A multi-agent system is a computerized system composed of multiple interacting intelligent agents.
Multi-modal interaction - Multimodal ...
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How to represent players in a multi agent environment so each model can distinguish its own player
So I have 2 models trained with the DQN algorithm that I want to train in a multi-agent environment to see how they react with each other. The models were trained in an environment consisting of 0's ...
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How does Friend-or-Foe Q-learning intuitively work?
I read about Q-Learning and was reading about multi-agent environments. I tried to read the paper Friend-or-Foe Q-learning, but could not understand anything, except for a very vague idea.
What does ...
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Multi Agent Sokoban Search Solvers state of the art
I also asked this question here but I'm repeating it on this SE because I feel it is more relevant. No intention to spam.
I am researching into coding a solver for a variant of the Sokoban game with ...
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What is the best method to deal with heterogeneous multi agent system MAS?
Heterogeneity: Based on the heterogeneity of agents MAS can be divided into two categories namely: homogeneous and heterogeneous. Homogeneous MAS include agents that all have the same characteristics ...
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How to deal with the terminal state in SARSA in a multi-agent setting?
I'm training a SARSA agent to update a Q function, but I'm confused about how you handle the final state. In this case, when the game ends and there is no $S'$.
For example, the agent performed an ...
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Convergence in multi-agent environment
I have a multi-agent environment where agents are trying to optimise the overall energy consumption of their group. Agents can exchange energy between themselves (actions for exchange of energy ...
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Algorithms for multiple agents problems
Can anyone recommend a reinforcement learning algorithm for a multi-agent environment?
In my simplified example, I'm implementing a Q-Learning system with different 10 agents. The agents compete for ...
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Can Q-learning working in a multi agent environment where every agent learns a behaviour independently?
I am currently exploring multi-agent reinforcement learning. I have multiple agents that communicate with each other and a central service that maintains the environment state.
The central service ...
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Agent collision avoidance java
I am working with a project which is a agent based pedestrian simulation in Java and its is animated with the help of JavaFX. I've tried to read all the social force model papers but my understanding ...
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How to choose evaluation functions for features, when network effects are in place (multi-agent systems)?
So, I have this huge amount of data, which has 7 vector features (float from 0 to 1). I am trying to build a kind of recommendation system, with a twist (it uses agents and negotiations and narratives;...
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To what extent can artificially intelligent agents reliably predict trends in financial markets?
I've gotten curious about this topic and am wondering what the stack exchange community has to say about it. Also, does anyone know of any professors/researchers who have published papers pertaining ...
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How can I design a hierarchy of agents each of which with different goals?
I read some light material earlier about the possibility of building a hierarchy of agents, where the agents at the leaves solve primitive tasks while higher-level agents are optimized for ...
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