10
votes
Accepted
What are some examples of intelligent agents for each intelligent agent class?
There are no distinguishable hardware examples for each IA class. The same mobile robot architecture with proper sensors can be implemented to behave as any IA class. The way you can determine the ...
- 146
9
votes
Accepted
How can thousand-robot swarm coordinate their moves without bumping into each other?
There has been quite a few approaches to achieve such kind of distributed coordination. I present here one of them, for its generality and simplicity (that makes it easy to remember too). But first, ...
- 1,510
6
votes
Accepted
How does the Alpha Zero's move encoding work?
Let's do the code, so all the details are down.
Encoding dictionary:
...
- 2,552
5
votes
Accepted
Are there any decentralized examples of AI systems with blockchain technology?
Swarm intelligence is the term for systems where relatively simple agents work together to solve a complicated problem in a decentralized fashion.
In general, distributed computing methods are very ...
- 4,272
3
votes
Why does Alpha Zero's Neural Network flip the board to be oriented towards the current player?
I am not an expert in RL. I have been playing Go for some years.
Let's quote from AlphaZero's paper first:
Aside from
komi, the rules of Go are also invariant to colour transposition; this knowledge ...
- 131
3
votes
Accepted
Can AlphaZero considered as Multi-Agent Deep Reinforcement Learning?
Depends on perspective.
On one hand, you have an agent playing in an environment with another agent also evolving. This falls under the definition of Multi-Agent Learning, as can be seen with works ...
- 906
3
votes
Accepted
Convergence in multi-agent environment
Does it have to do with the reward function?
This seems likely to me. You have chosen a reward that is unusual in that it cross-links episodes. It is not really a reinforcement problem to optimise ...
- 33k
3
votes
Are there any decentralized examples of AI systems with blockchain technology?
I think the best example of AI being deployed on the blockchain is SingularityNET. They just had a successful token sell where they sold out of their AGI token which will be able to be used to ...
- 307
3
votes
To what extent can artificially intelligent agents reliably predict trends in financial markets?
This is a highly relevant question as market trends have become more emphasized over the fundamentals of individual companies, and algorithmic trading has proven to be quite effective, particularly in ...
- 6,235
2
votes
To what extent can artificially intelligent agents reliably predict trends in financial markets?
A couple of thoughts:
Humans can't reliably predict trends in the stock market, so expecting AI's to do so is probably unreasonable.
The above would be more true if it were proven that the movement ...
- 3,767
2
votes
Accepted
Can Q-learning working in a multi agent environment where every agent learns a behaviour independently?
Not particularly sure what you are asking, so the question that I will be answering is this:
Can Q learning be used to estimate a value that depends on another value in the Q Learning Matrix even ...
- 138
2
votes
Agent collision avoidance java
A very efficient approach to what you are trying to do is velocity obstacles.
Assuming two agents use constant velocity motion vectors, a velocity obstacle models a geometric region in which if the ...
- 146
2
votes
Accepted
How would one implement a multi-agent environment with asynchronous action and rewards per agent?
The cleanest solution from a theoretical point of view is to switch over to a hierarchical framework, some framework that supports temporal abstraction. My favourite one is the options framework as ...
- 10.4k
2
votes
Accepted
What is the difference between multi-agent and multi-modal systems?
An agent is a concept, which can have slightly different meanings, abilities or instantiations depending on the context. However, given the purpose of this website, I will use and refer to the ...
- 41.1k
2
votes
Accepted
How to deal with the terminal state in SARSA in a multi-agent setting?
The SARSA update rule looks like:
$$Q(S, A) \gets Q(S, A) + \alpha \left[ R + \gamma Q(S', A') \right].$$
Very similar, the $Q$-learning update rule looks like:
$$Q(S, A) \gets Q(S, A) + \alpha \left[ ...
- 10.4k
2
votes
What is the relation between multi-agent learning and reinforcement learning?
I think there is an intersection. There are problems that are in reinforcement learning and in learning in multi-agent systems. There are problems in reinforcement learning, but not exactly in multi-...
- 187
2
votes
Accepted
Shouldn't the utility function of two-player zero-sum games be in the range $[-1, 1]$?
it can be either. If you consider the lack of reward as "penalty" then getting 0 reward is bad.
if you use a value estimator through a neural network, the range of rewards will dictate the squashing ...
- 201
2
votes
Accepted
Policy Gradient ( Advantage actor-critic) for multiple simultaneous continuous actions
Sounds like you have several problems with the way your policy is parametrized.
You don't have to use the multivariate normal distribution. It can work, and probably others have done it already (if ...
- 1,301
2
votes
Accepted
Why does Alpha Zero's Neural Network flip the board to be oriented towards the current player?
There is a single neural network that guides self-plays in the Monte Carlo Tree Search algorithm. The neural network gets the current state of the board $s$ as an input and outputs current policy $\pi(...
- 2,552
2
votes
Accepted
How can rewards and loss calculation be extended to multiple agents in a vanilla policy gradient RL setting?
Yes, this can be done and is widely applied in recent literature on multi-agent RL, at least with the collaborative setting where agents are optimizing a shared reward. This is also known as parameter ...
- 579
2
votes
Accepted
Q learning (DQN) strategy for a multiplayer zero-sum game
This works, and is used as a standard approach for two player zero-sum games in reinforcement learning. As you stated, it is a combination of reinforcement learning with Minimax optimisation.
A very ...
- 33k
1
vote
Can a Reinforcement Learning problem with multiple simultaneous actions be formalized as a Multiagent Partially Observable Markov Decision Process?
I guess it depends on what the goal is. If the goal is a general reward function, this formulation as an MPOMDP could make sense. One way to think about this, is as a way of modeling a general (...
- 11
1
vote
Is there multi-agent reinforcement learning model in which (some of the) reward is given by other agent and not by the external environment?
This is mostly an implementation architecture problem, and the thing is that basically you can implement anything in the traditional setting. To do so instead of having ...
- 131
1
vote
How to train the NN of simple agents given a reward system?
You might be able to glean what you want from Chapter 13 or Sutton & Barto's Reinforcement Learning: An Introduction, which deals with policy gradient algorithms, and includes pseudocode for a ...
- 33k
1
vote
How to handle a changing in the Reinforcement Learning environment where there is increasing or decreasing in number of agents?
I depends on your overall model architecture (and problem specification). As I understand it, you take the observations of all agents together and feed it into one model, a central controller, which ...
- 825
1
vote
Agents meeting in a directed connected graph
It's not possible to solve version 1) of the problem in general. To see why, consider a graph with 2 cities, and 2 agents, where the agents start in opposite nodes. Since both agents need to move ...
- 9,362
1
vote
Accepted
Would a general-purpose AI need to collaborate?
In my answer, I have often switched between AGI and ASI for reference. This is fine as an AGI will reach ASI as it is optimizing itself and learning.
I think it is not only important by necessary that ...
- 2,023
1
vote
How to choose evaluation functions for features, when network effects are in place (multi-agent systems)?
In a sense it seems like I am thinking some kind of "Deep Agent-Based Modeling", where it is okay to have network effects on the lowest layer (which would only evaluate the matching of the generated ...
- 278
1
vote
To what extent can artificially intelligent agents reliably predict trends in financial markets?
There is quite some research done by Hans-Georg Zimmermann, who has programmed Neural Networks for Siemens since some 20 years in order to predict Stock markets. He wrote some books on it, too, though ...
- 151
1
vote
Accepted
How can I design a hierarchy of agents each of which with different goals?
In the context of reinforcement learning, the idea of modeling your goal-oriented problem as a hierarchy of multiple sub-problems is called hierarchical reinforcement learning, which gives rise to ...
- 41.1k
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