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intermediate

Policy Learning and Deep Reinforcement Learning

Move from tabular value updates to parameterized policies, policy gradients, actor-critic reasoning, and the role of deep learning in scaling representations.

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Articles in Policy Learning and Deep Reinforcement Learning

Follow the learning path in order or jump directly to the RL concept or experiment you need.

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intermediate

9 min read

Deep Reinforcement Learning Explained: What Neural Networks Add

When you hear "deep reinforcement learning," it's tempting to picture a neural network that somehow learned to think for itself. The network isn't doing…

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intermediate

12 min read

Policy Gradient Methods Explained: Learning Actions Directly

You've built a Q-learning agent. It works. For each state, you compare action values, pick the best one, and call that a policy. Then someone mentions…

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intermediate

10 min read

Actor-Critic Methods Explained: Why Policies Need a Baseline

If you've worked through policy gradients, you've felt the problem: one lucky episode can make a mediocre action look brilliant, and the next update swings…

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intermediate

11 min read

Value-Based vs Policy-Based Reinforcement Learning

The real question is not which family of algorithms is better. It is which one fits the structure of your problem—your action space, your data budget, and…

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advanced

10 min read

Reinforcement Learning for LLMs: What the RL Loop Looks Like

If you have worked through policy gradients and deep RL, the language-model post-training landscape looks like a different discipline. RLHF. PPO. DPO.…

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intermediate

10 min read

When Deep Reinforcement Learning Is the Wrong Tool

Deep RL is not the destination of reinforcement learning. It is a specialized tool with a steep cost profile, and most learners reach for it before they…

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intermediate

11 min read

Stabilizing Deep Q-Learning: Replay, Target Networks, and Moving Targets

Tabular Q-learning converges reliably. Swap the table for a neural network, and the same update can oscillate, blow up, or quietly forget everything it…

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intermediate

9 min read

Parameterized Policies in Reinforcement Learning: From Tables to Networks

A policy network is not a bigger table. That is the mental model to dismantle before you touch policy-gradient code, because it quietly teaches you the…

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intermediate

7 min read

Tabular RL vs Function Approximation: What Changes When Tables Become Networks?

Your Q-table worked beautifully on the grid world. Every cell held a number, every update made sense, and you could read the agent's beliefs like an open…

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intermediate

11 min read

Proximal Policy Optimization Explained: Why Clipped Updates Can Stabilize Policy Learning

A policy-gradient update is a stride, not a leap. Take too long a step, and you can land somewhere worse than where you started—sometimes so much worse…

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advanced

9 min read

Preference-Based Reinforcement Learning Explained: Learning What Outcomes People Prefer

Hand-written rewards get hacked. Preference-based reinforcement learning replaces that fragile numeric signal with something that feels safer—human…

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advanced

9 min read

Reward Models From Human Preferences: Turning Comparisons Into a Learning Signal

A reward model is a compression. Thousands of noisy human comparisons get squeezed into one scalar function, and the compression itself is where meaning…

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intermediate

12 min read

Collecting Preference Data for RL: Better Comparisons, Better Reward Models

More preference labels do not automatically mean a better reward model. The reward model is only as sharp as the comparisons that feed it.

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advanced

10 min read

Reward Model Evaluation: When RL Optimizes the Proxy Instead of the Goal

The reward curve climbs steadily. The training loss falls exactly as expected. And the outputs get visibly worse. Human reviewers start rejecting what the…

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advanced

7 min read

Recurrent Policies and Memory in Reinforcement Learning

A memoryless policy keeps repeating the same mistake because each observation hides the information it needs. The reflex is to "just add an RNN." The real…