Reinforcement Learning Digest Part 4: Deep Q-Network(DQN) and Double Deep Q-Networks(DDQN)

In last article, we have discussed Q-learning and we have seen its desirable convergence attributes. Never the less, Q-learning has one fundamental limitation preventing it from being applicable to more complex RL tasks. During learning, Q-learning keeps the Q-value for every state-action pair. In FrozenLake with 4x4 grid, there are 4 actions leading to Q-table size of 4x4x4 = 64. Size of Q-table can grows linearly proportional to number of states. states. This becomes limiting very quickly for RL tasks…

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Technical lead of IBM Cognos recommenders system

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