In the previous post, we introduced the breakthrough concept of combining deep learning and reinforcement learning (RL). Instead of recording estimated Q-values in a table, which is intractable for large or continuous state spaces, we approximated those Q-values using a neural network. This simple act spawned the current generation of deep RL, paving the way…

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Previously, we looked at how Q-learning used off-policy temporal difference (TD) updates to converge on an optimal policy. This reinforcement learning (RL) algorithm works surprisingly well, but it requires the environment to have relatively small, discrete state and action spaces. The Q-table can quickly grow to intractable sizes with environments that have a large number…

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One of the biggest breakthroughs in reinforcement learning (RL) occurred in 1989 with Chris Watkins’s paper, Learning from Delayed Rewards. In it, he proposed Q-learning, which decouples the experience gathered from the policy update. In other words, the agent can collect experience using one policy (called the behavior policy) while updating a different policy (called…

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In the previous post, we saw how temporal difference (TD) learning updated value predictions in the middle of an episode rather than waiting to the very end, like we do with Monte Carlo (MC) methods. If you recall, the TD(0) algorithm updates value estimates after every step using a single reward in order to bootstrap…

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Temporal Difference (TD) learning is one of the foundational concepts in reinforcement learning (RL). It combines the notion of updating estimates before the final outcome is known, similar to how dynamic programming (DP) works, with the notion of learning directly from experience, like we saw with the Monte Carlo (MC) methods in part 7. MC…

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