What is the advantage of Deterministic Policy Gradient over Stochastic Policy Gradient? : <p>Deep Deterministic Policy Gradient (<a href="https://arxiv.org/pdf/1509.02971.pdf" rel="noreferrer">DDPG</a>) is the state-of-the-art method for reinforcement learning when the action space is continuous. Its core algorithm is <a href="http://www.jmlr.org/proceedings/papers/v32/silver14.pdf" rel="noreferrer">Deterministic Policy Gradient</a>.</p>

<p>However, after reading the papers and listening to the talk (<a href="http://techtalks.tv/talks/deterministic-policy-gradient-algorithms/61098/" rel="noreferrer">http://techtalks.tv/talks/deterministic-policy-gradient-algorithms/61098/</a>), I still cannot figure out what the fundamental advantage for Deterministic PG over Stochastic PG is. The talk says it is more suitable for high dimensional actions and easier to train, but why is that?</p>
