… In addition to being well-motivated by recent studies on the behaviour of deep policy gradient
algorithms, we demonstrate that this modification is both theoretically sound and intuitively …

… w(st,at), and therefore, we call this term residual likelihood ratio gradient… policy gradient
methods, a family of policy gradient algorithms that allow mixing off-policy learning with on-policy …

… policy gradient methods to learn πθ even if the initial policy π is not differentiable. There
are two ways to see the role of the residual… , we use Deep Deterministic Policy Gradients (DDPG) …

… reduction in deep policy gradient methods. We demonstrate that more variance reduction
can … Learning value functions in deep policy gradients using residual variance. In ICLR 2021-…

… with prior work on policy gradients – relating to the … : Deep Compatible Function Approximation
Our main result is that the deviator’s value gradient is compatible with the policy gradient …

Policy-gradient methods in Reinforcement Learning(RL) are very universal and widely applied
in practice but their performance suffers from the high variance of the gradient … of Deep RL…

… model to control the gradient variance, SN has also been applied in deep RL to value
functions in order to enable deeper neural nets [8] or regulate the value-aware model error [72]. …

… policy gradient. Summing these gradients over the trajectory gives the total policy gradient.
… Since SVG(1) is modelbased, we can also use Bellman residual minimization [3]. In practice…

… In particular, we regularize the behavior of the deep policy to be similar to a policy prior, ie,
we … This work focuses on policy gradient RL methods, which estimate the gradient of the …

… of EPG based on softmax policies. We also establish a new general policy gradient
theorem, of which the stochastic and deterministic policy gradient theorems are special cases. …