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Bookmark or Share- Understanding the role of the discount factor in reinforcement learningAn MDP provides a mathematical framework for modeling decision-making situations where outcomes are partly random and partly under the control of the decision maker. An MDP is defined via a state space $\mathcal{S}$ , an action space $\mathcal{A}$ , a function of transition probabilities between states (conditioned to the action taken by the ...
- Convergence of Value Iteration in Reinforcement LearningThe MDP I have is one with 600 states and one terminal state which I want to avoid. The empirical (estimated) rewards r(s,a,s') are positive or zeros on all states (s') for every action except the terminal state which is negative.
- What is the difference between Reinforcement Learning(RL) and Markov ...Specifically, MDP describes a fully observable environment in RL, but in general the environment might me partially observable (see Partially observable Markov decision process (POMDP). So RL is a set of methods that learn "how to (optimally) behave" in an environment, whereas MDP is a formal representation of such environment.
- Why is the optimal policy in Markov Decision Process (MDP), independent ...In my opinion, any policy that achieves the optimal value is an optimal policy. Since the optimal value function for a given MDP is unique, this optimal value function actually defines a equivalent class over the policy space, i.e., those whose value is optimal are actually equivalent.
- Real-life examples of Markov Decision ProcessesOnce the MDP is defined, a policy can be learned by doing Value Iteration or Policy Iteration which calculates the expected reward for each of the states. The policy then gives per state the best (given the MDP model) action to do.
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