Web Reference: TD3 adds noise to the target action, to make it harder for the policy to exploit Q-function errors by smoothing out Q along changes in action. Together, these three tricks result in substantially improved performance over baseline DDPG. The twin-delayed deep deterministic (TD3) policy gradient algorithm is an off-policy actor-critic method for environments with a continuous action-space. A TD3 agent learns a deterministic policy while also using two Q-value function critics to estimate the value of the optimal policy. We include an implementation of DDPG (DDPG.py), which is not used in the paper, for easy comparison of hyper-parameters with TD3. This is not the implementation of "Our DDPG" as used in the paper (see OurDDPG.py). Algorithms which TD3 compares against (PPO, TRPO, ACKTR, DDPG) can be found at OpenAI baselines repository.
YouTube Excerpt: In this tutorial, we continue building the Twin Delayed Deep Deterministic Policy Gradient (
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