Build the math needed to compare policies without heavy theory. You will use probability distributions, expectations, sampling, entropy, and cross-entropy in small policy examples.

Create tabular policies for tiny environments and measure how well they act. This gives you a concrete policy to copy, improve, and later distill.

Use value functions, Q-values, advantage estimates, and Bellman updates to judge actions. These ideas explain what many teacher policies know beyond their final action choice.

Train small policies with policy gradients and actor-critic methods. You will see where logits, action probabilities, and gradients come from before using them as distillation targets.

See how policy distillation grew from imitation learning, model compression, and deep reinforcement learning. The chapter connects early teacher-student methods to current uses in robotics, games, and language models.

Set up Gymnasium-style environments, PyTorch or JAX models, replay files, seeds, metrics, and experiment tracking. The focus is a simple, repeatable lab setup that makes distillation results trustworthy.

Train or load a teacher policy and inspect what it produces: actions, logits, probabilities, value estimates, hidden states, and demonstrations. You will decide which teacher signals are worth passing to a student.

Copy a teacher from recorded state-action pairs using behavior cloning. This chapter covers dataset quality, covariate shift, class imbalance, and why a student can fail even when training loss looks good.

Distill a teacher’s full action distribution instead of only its chosen action. You will use temperature, KL divergence, cross-entropy, softened labels, and confidence filtering to make the student learn richer behavior.

Train the student on states it actually visits, not only states from the teacher’s old dataset. This chapter covers on-policy rollouts, teacher queries during student play, DAgger-style aggregation, and safety limits during data collection.

Shape the student’s objective with reward, imitation loss, entropy, value loss, and regularization. You will practice balancing losses so the student copies well without becoming brittle or overconfident.

Use checkpoints, replay buffers, curriculum schedules, and staged teachers to make training reliable. The chapter covers divergence, catastrophic mistakes, teacher overfitting, and practical debugging signals.

Combine several teacher policies into one student that can handle more tasks or contexts. You will work with task labels, gating, mixture targets, conflict resolution, and evaluation across task families.

Distill from Q-learning agents, actor-critic agents, and distributional RL agents. This chapter shows how action values, advantages, return distributions, and uncertainty estimates can become useful student targets.

Use fixed datasets when live environment interaction is expensive or unsafe. You will handle coverage gaps, conservative policy constraints, offline evaluation, and the limits of learning from logged behavior.

Compress a student for real deployment with smaller networks, pruning, quantization, batching, and latency tests. The goal is a policy that is not just accurate, but fast, cheap, and reliable where it will run.

Apply distillation to policies with memory, attention, and long context. This chapter covers recurrent policies, transformer policies, sequence decision models, and the extra care needed when the teacher’s behavior depends on history.

Use distillation in robotics where data is physical, noisy, and risky. You will cover sim-to-real transfer, privileged teacher information, sensor limits, safety stops, and evaluation on real or realistic control tasks.

Handle modern diffusion policies used for action generation in robotics and control. This chapter covers why diffusion policies can be strong teachers, how consistency and progressive distillation speed them up, and where imitation loss differs from standard action cloning.

Distill policies used in reinforcement learning from human feedback and preference-trained systems. You will cover reward models, reference policies, KL penalties, rejection sampling, direct preference methods, and how aligned behavior is compressed into deployable models.

Measure whether the distilled policy really works with returns, success rates, robustness tests, calibration, latency, and cost. This chapter also covers statistical confidence, ablations, and fair comparisons against the teacher.

Follow a full project from goal and environment choice through teacher training, data collection, student training, evaluation, compression, and deployment notes. You will produce a reproducible distillation report and a working student policy.

Protect users and systems from unsafe copied behavior, hidden teacher bias, reward hacking, privacy leaks, and uncontrolled deployment. The chapter covers audit trails, model cards, dataset consent, red-team tests, and rollback plans.

Map the roles, skills, and artifacts that show real ability in this area. You will plan portfolio projects, papers to follow, benchmarks to track, and paths into RL engineering, robotics, AI safety, or applied ML work.