rl.ipynb

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    "# Reinforcement Learning\n",
    "\n",
    "This IPy notebook acts as supporting material for **Chapter 21 Reinforcement Learning** of the book* Artificial Intelligence: A Modern Approach*. This notebook makes use of the implementations in rl.py module. We also make use of implementation of MDPs in the mdp.py module to test our agents. It might be helpful if you have already gone through the IPy notebook dealing with Markov decision process. Let us import everything from the rl module. It might be helpful to view the source of some of our implementations. Please refer to the Introductory IPy file for more details."
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    "## Review\n",
    "Before we start playing with the actual implementations let us review a couple of things about RL.\n",
    "\n",
    "1. Reinforcement Learning is concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. \n",
    "\n",
    "2. Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Further, there is a focus on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).\n",
    "\n",
    "-- Source: [Wikipedia](https://en.wikipedia.org/wiki/Reinforcement_learning)\n",
    "\n",
    "In summary we have a sequence of state action transitions with rewards associated with some states. Our goal is to find the optimal policy (pi) which tells us what action to take in each state."
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