Results 11 to 20 of about 6,292,535 (287)
Off-Policy Proximal Policy Optimization
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Proximal Policy Optimization (PPO) is an important reinforcement learning method, which has achieved great success in sequential decision-making problems. However, PPO faces the issue of sample inefficiency, which is due to the PPO cannot make use of off-
Yin, Yilong +3 more
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Constrained Proximal Policy Optimization
CoRR, 2023 The problem of constrained reinforcement learning (CRL) holds significant importance as it provides a framework for addressing critical safety satisfaction concerns in the field of reinforcement learning (RL). However, with the introduction of constraint
Xuan, Chengbin +3 more
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Behavior Proximal Policy Optimization
CoRR, 2023 Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-critic methods perform poorly due to the overestimation of out-of-distribution state-action pairs.
Liu, Jinxin +4 more
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Gradient Informed Proximal Policy Optimization
Advances in Neural Information Processing Systems 36, 2023 We introduce a novel policy learning method that integrates analytical gradients from differentiable environments with the Proximal Policy Optimization (PPO) algorithm.
Son, Sanghyun +4 more
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Visual Navigation with Asynchronous Proximal Policy Optimization in Artificial Agents
Journal of Robotics, 2020 Vanilla policy gradient methods suffer from high variance, leading to unstable policies during training, where the policy’s performance fluctuates drastically between iterations.
Fanyu Zeng, Chen Wang
doaj +2 more sources
Transductive Off-policy Proximal Policy Optimization [PDF]
CoRRProximal Policy Optimization (PPO) is a popular model-free reinforcement learning algorithm, esteemed for its simplicity and efficacy. However, due to its inherent on-policy nature, its proficiency in harnessing data from disparate policies is ...
Xing, Junliang +4 more
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Proximal Policy Optimization with Adaptive Exploration [PDF]
CoRRProximal Policy Optimization with Adaptive Exploration (axPPO) is introduced as a novel learning algorithm. This paper investigates the exploration-exploitation tradeoff within the context of reinforcement learning and aims to contribute new insights ...
Lixandru, Andrei
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Beyond the Boundaries of Proximal Policy Optimization
CoRRProximal policy optimization (PPO) is a widely-used algorithm for on-policy reinforcement learning. This work offers an alternative perspective of PPO, in which it is decomposed into the inner-loop estimation of update vectors, and the outer-loop ...
Huszár, Ferenc +4 more
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Proximal policy optimization with model-based methods
Journal of Intelligent & Fuzzy Systems, 2022 Model-free reinforcement learning methods have successfully been applied to practical applications such as decision-making problems in Atari games. However, these methods have inherent shortcomings, such as a high variance and low sample efficiency.
Zhang X(张鑫) +4 more
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Preferential proximal policy optimization in reinforcement learning [PDF]
, 2023 The Proximal Policy Optimization (PPO), a policy gradient method, excels in reinforcement learning with its ”surrogate” objective function and stochastic gradient ascent.
Balasuntharam, Tamilselvan
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