This paper introduces the DDPG algorithm which builds on the existing DPG algorithm from classic RL theory. The main idea is to define a deterministic policy, or nearly deterministic, for situations where the environment is very sensitive to suboptimal actions, and one action setting usually dominates in each state. This showed good performance, but could not beat algorithms such as PPO until the additions of SAC were added. SAC adds an entropy penalty which essentially penalizes uncertainty in any states. Using this, the deterministic policy gradient approach performs well.

Paper that introduced the PPO algorithm. PPO is, in a way, a response to the TRPO algorithm, trying to use the core idea but implement a more efficient and simpler algorithm.

TRPO defines the problem as a straight optimization problem, no learning is actually involved.

Tom Schaul, John Quan, Ioannis Antonoglou and David Silver. "PRIORITIZED EXPERIENCE REPLAY", ICLR, 2016.