KURT DRIESSENS and SASO DZEROSKI (2004) "Integrating Guidance into Relational Reinforcement Learning" Machine Learning, 57, pp.271-304,
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<a possible brief explanation of RL in OHP>


<in Abstract>
Reinforcement learning, and Q-learning in particular, encounter two major problems when dealing with large state spaces. First, learning the Q-function in tabular form may be infeasible because of the excessive amount of memory needed to store the table, and <<because the Q-function only converges after each state has been visited multiple times>>. Second, rewards in the state space may be so sparse that with random exploration they will only be discovered extremely slowly. ...

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"In reinforcement learning, an agent tries to learn a policy, i.e., how to select an action in a given state of the environment, so that it maximizes the total amount of reward it receives when interacting with the environment."
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"Q-learning (Watkins, 1989) is a form of reinforcement learning where the optimal policy is learned implicitly in the form of a Q-function, which takes a state-action pair as input and outputs the quality of the action in that state. The optimal action in a given state is then the action with the largest Q-value."
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One of the main limitations of standard Q-learning is related to the number of different state-action pairs that may exist. The Q-function can in principle be represented as a table with one entry for each state-action pair.
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"Using random exploration through the search space, rewards may simply never be encountered."
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"Thus a mix between the classical unsupervised Q-learning and (supervised) behavioral cloning is obtained." <ideally unsupervised is wanted but actually supervised is needed more or less>