… We introduce a parameterized family of policy gradient methods that interpolate between
on-policy and off-policy learning. Such methods are in general biased, but we show that the …

… In the following, we show that the Beta policy is bias-free and a better choice than the … policy
gradient of both policies and show that as with the Gaussian policy, Natural Policy Gradient …

… You will evaluate methods including Cross-entropy and policy gradients, before applying
them to real-… This book is an introduction to deep reinforcement learning (RL) and requires no …

We study how the behavior of deep policy gradient … : gradient estimation, value prediction,
and optimization landscapes. Our results show that the behavior of deep policy gradient …

… policy 𝜋∅ is updated by taking the expected return gradient ∇∅ 𝐽(∅). For policy gradient
techniques, the policy is … The update is applied using deterministic policy gradient algorithm: …

… , Policy Gradient Methods, explains how to use neural networks to learn the policy for decision
making in reinforcement learning. … policy gradient methods on a continuous action space. …

… we call Deep Soft Policy Gradient (DSPG). First, we propose soft policy gradient under
maximum … Second, soft policy gradient is combined with soft Bellman equation by employing two …

… Policy gradient methods frame the goal of maximizing return … policy gradient method is
Deep Deterministic Policy Gradient (… art deep reinforcement learning algorithms of the off-policy …

… in deep policy gradient algorithms through a case study on two popular algorithms: Proximal
Policy Optimization (PPO) and Trust Region Policy … gains in deep reinforcement learning. …

Despite the recent advances of deep reinforcement learning (DRL), agents trained by DRL
tend to be brittle and sensitive to the training environment, especially in the multi-agent …