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The game of TIC-TAC-TOE can be modelled as a non-deterministic Markov decision process (MDP) if, and only if:
The opponent is considered part of the environment. This is a reasonable approach when the goal is to solve playing against a specific opponent.
The opponent is using a stochastic policy. Stochastic policies are a generalisation that include ...
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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 such as
Michael Bowling and Manuela Veloso. Multiagent learning using a variable learning
rate. Artificial Intelligence, 136(2):215 – 250, 2002.
Michael Bowling....
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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-agent systems. And there is learning in multi-agent systems that is not through reinforcement learning. For sort you can say: multi-agent reinforcement learning. ...
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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 Env<->Agent1<->Agent2, you should have Agent1<->SuperEnv<->Agent2 where SuperEnv contains Env, and simply uses the reward given to SuperEnv by Agent1 and passes it to ...
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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 variety of agents based on linear approximation using softmax regression. From your description, you appear to be using - or should consider - softmax regression ...
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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 then predicts the action per available agent.
I believe that this varying number of applicable observations (depending on the number of currently present agents) ...
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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 function you use for the output layer
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