Reinforcement learning with continuous action spaces, including action distributions, bounded actions, and continuous-control actor-critic methods.
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10 articles in this tag.
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…
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The deterministic actor loss is one line: maximize $Q(s, \mu_\theta(s))$. It looks like a trick because the log-probability term — the entire engine of the…
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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…
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Every actor-critic user hits the same wall shortly after their first working implementation: the advantage estimate looks fine in theory, but the policy…
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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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Your SAC implementation is probably correct. That is exactly why the failed run is so frustrating: the losses move, the entropy term behaves, and the agent…
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Most actor-critic methods force you to choose between sample efficiency and stable exploration. Soft Actor-Critic (SAC) refuses the trade—and that refusal…
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A SAC actor hands you two tensors: the action you just sent to the environment, and logp_pi. That second number is not the Gaussian log-density of the…
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DDPG doesn't fail because its policy is weak. It fails because its critic quietly starts lying, and the policy trusts every word.
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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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