Add to ORKGThe recent remarkable progress of deep reinforcement learning (DRL) stands on regularization of policy for stable and efficient learning. A popular method, named proximal policy optimization (PPO), has been introduced for this purpose. PPO clips density ratio of the latest and baseline policies with a threshold, while its minimization target is unclear. As another problem of PPO, the symmetric threshold is given numerically while the density ratio itself is in asymmetric domain, thereby causing unbalanced regularization of the policy. This paper therefore proposes a new variant of PPO by considering a regularization problem of relative Pearson (RPE) divergence, so-called PPO-RPE. This regularization yields the clear minimization target, whi...
Proximal Policy Optimization (PPO) is a highly popular policy-based deep reinforcement learning (DRL...
This paper introduces two simple techniques to improve off-policy Reinforcement Learning (RL) algori...
Proximal Policy Optimization (PPO) is an important reinforcement learning method, which has achieved...
Deep reinforcement learning (DRL) is one of the promising approaches for introducing robots into com...
Proximal policy optimization (PPO) has become one of the most popular deep reinforcement learning (D...
Policy-based reinforcement learning algorithms are widely used in various fields. Among them, mainst...
The Policy Gradient approach is one of the deep reinforcement learning families that combines deep n...
Policy optimization is a fundamental principle for designing reinforcement learning algorithms, and ...
Proximal Policy Optimization (PPO) is a highly popular model-free reinforcement learning (RL) approa...
Reinforcement learning is a family of machine learning algorithms, in which the system learns to mak...
We introduce a new constrained optimization method for policy gradient reinforcement learning, which...
First-order gradient descent is to date the most commonly used optimization method for training deep...
In the field of reinforcement learning, we propose a Correct Proximal Policy Optimization (CPPO) alg...
Proximal policy optimization (PPO) has yielded state-of-the-art results in policy search, a subfield...
Marginalized importance sampling (MIS), which measures the density ratio between the state-action oc...
Proximal Policy Optimization (PPO) is a highly popular policy-based deep reinforcement learning (DRL...
This paper introduces two simple techniques to improve off-policy Reinforcement Learning (RL) algori...
Proximal Policy Optimization (PPO) is an important reinforcement learning method, which has achieved...
Deep reinforcement learning (DRL) is one of the promising approaches for introducing robots into com...
Proximal policy optimization (PPO) has become one of the most popular deep reinforcement learning (D...
Policy-based reinforcement learning algorithms are widely used in various fields. Among them, mainst...
The Policy Gradient approach is one of the deep reinforcement learning families that combines deep n...
Policy optimization is a fundamental principle for designing reinforcement learning algorithms, and ...
Proximal Policy Optimization (PPO) is a highly popular model-free reinforcement learning (RL) approa...
Reinforcement learning is a family of machine learning algorithms, in which the system learns to mak...
We introduce a new constrained optimization method for policy gradient reinforcement learning, which...
First-order gradient descent is to date the most commonly used optimization method for training deep...
In the field of reinforcement learning, we propose a Correct Proximal Policy Optimization (CPPO) alg...
Proximal policy optimization (PPO) has yielded state-of-the-art results in policy search, a subfield...
Marginalized importance sampling (MIS), which measures the density ratio between the state-action oc...
Proximal Policy Optimization (PPO) is a highly popular policy-based deep reinforcement learning (DRL...
This paper introduces two simple techniques to improve off-policy Reinforcement Learning (RL) algori...
Proximal Policy Optimization (PPO) is an important reinforcement learning method, which has achieved...