Shawn Hymel

While we looked at the surrogate objective in the previous post, we’re actually going to return to the discussion of advantage functions from part 14.

We are reaching the end of our series, but we still have a few steps to do in our reinforcement learning (RL) journey before we can fully flesh out proximal policy optimization (PPO).

In the previous post, we ended with a straightforward application of the policy gradient in the REINFORCE algorithm, which proves to be a useful stepping stone in our deep reinforcement learning (RL) journey.

In the previous post, we showed how we can substitute our usual ε-greedy policy with a parameterized approximation (often a neural network), we then derived the policy gradient theorem required to optimize this approximator function, and demonstrated how…

In the previous post, we introduced the breakthrough concept of combining deep learning and reinforcement learning (RL).

Previously, we looked at how Q-learning used off-policy temporal difference (TD) updates to converge on an optimal policy.

One of the biggest breakthroughs in reinforcement learning (RL) occurred in 1989 with Chris Watkins’s paper, Learning from Delayed Rewards.

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.

Temporal Difference (TD) learning is one of the foundational concepts in reinforcement learning (RL).

In this second post on reinforcement learning (RL), we build on the introduction from part 1 by revisiting the idea of a reward and building up to the idea of discounted returns.

Reinforcement learning (RL) is a field of study within machine learning (ML) concerned with developing intelligent agents that take actions in dynamic environments in order to maximize their rewards.