## deterministic policy gradient

analytical gradients by the learned model trade off between the variance of the the number of training epochs performed across data in the reply buffer) for the policy and value functions, respectively. Also we know the trajectories in the replay buffer are collected by a slightly older policy $$\mu$$. REINFORCE (Monte-Carlo policy gradient) relies on an estimated return by Monte-Carlo methods using episode samples to update the policy parameter $$\theta$$. Model-based methods allows for more efficient computations and faster convergence than model-free methods [37, 19, 18, 38]. Deterministic value gradient methods [39, 25, 15, 6, 9] compute the policy gradient through back propagation of the reward along a trajectory predicted by the learned model, which enables better sample efficiency.

This allows us to set up an efficient, gradient-based learning rule for a policy which exploits that fact.

Either $$\pi$$ or $$\mu$$ is what a reinforcement learning algorithm aims to learn. ∙ Here is a list of notations to help you read through equations in the post easily. where $$d^\pi(s)$$ is the stationary distribution of Markov chain for $$\pi_\theta$$ (on-policy state distribution under $$\pi$$). )\) as a baseline. It’s given by. experiments comparing DVPG with state-of-the-art methods on several standard Deterministic Policy Gradient Algorithms: Supplementary Material 2.For any stochastic policy ˇ, sup s ˆˇ(s)

(10) for infinite times is slow to converge. Here is a nice summary of a general form of policy gradient methods borrowed from the GAE (general advantage estimation) paper (Schulman et al., 2016) and this post thoroughly discussed several components in GAE , highly recommended. The behavior policy for collecting samples is a known policy (predefined just like a hyperparameter), labelled as $$\beta(a \vert s)$$.

As shown in Figure. or learn it off-policy-ly by following a different stochastic behavior policy to collect samples. When called, these should return: Interpolation factor in polyak averaging for target Twin Delayed Deep Deterministic (short for TD3; Fujimoto et al., 2018) applied a couple of tricks on DDPG to prevent the overestimation of the value function: (1) Clipped Double Q-learning: In Double Q-Learning, the action selection and Q-value estimation are made by two networks separately. SAC updates the policy to minimize the KL-divergence: where $$\Pi$$ is the set of potential policies that we can model our policy as to keep them tractable; for example, $$\Pi$$ can be the family of Gaussian mixture distributions, expensive to model but highly expressive and still tractable.