Explain advanced policy objectives, update constraints, and actor-critic algorithms through their mechanisms, stability tradeoffs, and continuous-control behavior.
Tutorials
Follow the learning path in order or jump directly to the RL concept or experiment you need.
You have trained a policy that works. The weights are tuned, the rewards are climbing, and the agent is finally doing something that looks like competence.…
Read tutorial
Most reinforcement learning tutorials treat randomness as a ladder: you need it to explore, and once training converges, you kick it away. Maximum-entropy…
Read tutorial
Most actor-critic methods force you to choose between sample efficiency and stable exploration. Soft Actor-Critic (SAC) refuses the trade—and that refusal…
Read tutorial
A stochastic policy averages over actions. A deterministic policy climbs the critic's slope at the single action it outputs. That swap—from averaging to…
Read tutorial
Every actor-critic user hits the same wall shortly after their first working implementation: the advantage estimate looks fine in theory, but the policy…
Read tutorial
DDPG doesn't fail because its policy is weak. It fails because its critic quietly starts lying, and the policy trusts every word.
Read tutorial
You move from discrete actions to continuous control, wire up a Gaussian policy head, and the agent either jitters uselessly around a target or slams…
Read tutorial