Reinforcement learning toy project

Posted by Betamoo on Stack Overflow See other posts from Stack Overflow or by Betamoo
Published on 2010-05-17T00:12:44Z Indexed on 2010/05/17 0:20 UTC
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reinforcement-learning

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machine-learning

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markov-models

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artificial-neural-network

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probability

My toy project to learn & apply Reinforcement Learning is:
- An agent tries to reach a goal state "safely" & "quickly"....
- But there are projectiles and rockets that are launched upon the agent in the way.
- The agent can determine rockets position -with some noise- only if they are "near"
- The agent then must learn to avoid crashing into these rockets..
- The agent has -rechargable with time- fuel which is consumed in agent motion
- Continuous Actions: Accelerating forward - Turning with angle

I need some hints and names of RL algorithms that suit that case..
- I think it is POMDP , but can I model it as MDP and just ignore noise?
- In case POMDP, What is the recommended way for evaluating probability?
- Which is better to use in this case: Value functions or Policy Iterations?
- Can I use NN to model environment dynamics instead of using explicit equations?
- If yes, Is there a specific type/model of NN to be recommended?
- I think Actions must be discretized, right?

I know it will take time and effort to learn such a topic, but I am eager to..
You may answer some of the questions if you can not answer all...
Thanks

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