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7

You should start with the general definition of Reinforcement Learning problem. And what Markov Decision Process is. DQN, A3C, PPO and REINFORCE are algorithms for solving reinforcement learning problems. These algorithms have their strengths and weaknesses depending on the details of the underlying problem. Multi-Armed Bandit is not even an algorithm - it ...


3

Would it be cheaper to have $N$ neural networks with a single real-valued output, one for each of the $N$ actions? I think the "No Free Lunch" theorem applies here, or something like it. Your proposed architecture would be an unusual choice in many cases, but might be more efficient in others. For instance, it could be more efficient in the ...


2

It is possible, at design time for a reinforcement learning problem, to allow for changes within an environment. You can make any element into a variable property of the state, that the agent can realistically be told at the start or sense from the environment. If you do add new variable to model the possibility of change: It allows the agent to learn to ...


2

The way I dealt with it was by giving a (very) strong negative reward when committing the mistake (here going under a red light) and the agent should learn to do not do this mistake anymore. It is often better to change actions, rewards or environment rather than acting on hyperparameters in my opinion, but I may be mistaken.


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When in an environment with competing agents, from the perspective of each agent, the environment becomes non-markovian. That occurs because each agent is constantly adapting its own strategy to other's actions, so a transition that occurred to a pair (s,a) before, resulting in a positive reward, might result in zero or negative reward in future iterations ...


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