# Deep Reinforcement Learning Algorithms: PPO, SAC, and World Models
Status: public
Confidence: medium (0.82) (verified)
Last verified: 2026-05-30
Generation: ai_structured


## TL;DR

Deep reinforcement learning is not one algorithm. PPO is a stable policy-gradient workhorse, SAC adds entropy-regularized off-policy learning, and Dreamer-style systems learn a world model before training behavior in imagined trajectories.

## Core Explanation

Reinforcement learning trains an agent to choose actions that increase expected future reward. Deep RL uses neural networks for policies, value functions, environment models, or all three. The main design choice is whether the algorithm updates a policy directly, learns value estimates, learns a model of the environment, or combines those pieces.

PPO constrains policy updates with a clipped objective so training does not jump too far in one step. SAC optimizes reward plus entropy, which encourages exploration and can improve robustness in continuous-control settings. DreamerV3 learns latent dynamics from experience and trains the policy using imagined rollouts instead of relying only on direct environment interaction.

## Related Articles

- [Actor-Critic Methods: A2C, A3C, PPO, and Deep Reinforcement Learning](../../computer-science/actor-critic-methods-a2c-a3c-ppo-and-deep-reinforcement-learning.md)
- [Optimization Algorithms for Deep Learning](../optimization-algorithms.md)
- [RLHF: Reinforcement Learning from Human Feedback](../rlhf.md)