Q Learning

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• Q Learning | Wikipedia
• Model Free Reinforcement learning algorithms (Monte Carlo, SARSA, Q-learning) | Madhu Sanjeevi (Mady) - Medium
• Reinforcement Learning (RL)
  • Monte Carlo (MC) Method - Model Free Reinforcement Learning
  • Markov Decision Process (MDP)
  • State-Action-Reward-State-Action (SARSA)
  • Deep Reinforcement Learning (DRL) DeepRL
  • Distributed Deep Reinforcement Learning (DDRL)
  • Deep Q Network (DQN)
  • Evolutionary Computation / Genetic Algorithms
  • Actor Critic
  • Hierarchical Reinforcement Learning (HRL)
• Gaming

When feedback is provided, it might be long time after the fateful decision has been made. In reality, the feedback is likely to be the result of a large number of prior decisions, taken amid a shifting, uncertain environment. Unlike supervised learning, there are no correct input/output pairs, so suboptimal actions are not explicitly corrected, wrong actions just decrease the corresponding value in the Q-table, meaning there’s less chance choosing the same action should the same state be encountered again. Quora | Jaron Collis

• Learning Rate: The learning rate or step size determines to what extent newly acquired information overrides old information. A factor of 0 makes the agent learn nothing (exclusively exploiting prior knowledge), while a factor of 1 makes the agent consider only the most recent information (ignoring prior knowledge to explore possibilities).

• Discount factor: The discount factor {\displaystyle \gamma } \gamma determines the importance of future rewards. A factor of 0 will make the agent "myopic" (or short-sighted) by only considering current rewards, i.e. {\displaystyle r_{t}} r_{t} (in the update rule above), while a factor approaching 1 will make it strive for a long-term high reward. If the discount factor meets or exceeds 1, the action values may diverge.

• Initial conditions (Q0): Since Q-learning is an iterative algorithm, it implicitly assumes an initial condition before the first update occurs. High initial values, also known as "optimistic initial conditions",[7] can encourage exploration: no matter what action is selected, the update rule will cause it to have lower values than the other alternative, thus increasing their choice probability. The first reward {\displaystyle r} r can be used to reset the initial conditions.