作者: Dysarthria 時間: 2025-3-21 22:47 作者: garrulous 時間: 2025-3-22 03:10 作者: chronology 時間: 2025-3-22 07:37 作者: 知識 時間: 2025-3-22 10:18 作者: 凝視 時間: 2025-3-22 15:10
Andreev, Alexander Ignatyevich (1887–1959), in which the performance of selected motions are evaluated and used as the references for making decisions in the following updates. The efficiency of the proposed approaches were demonstrated through the experiments for three commonly considered scenarios, where the comparisons have been made with several well studied strategies.作者: 凝視 時間: 2025-3-22 20:29
Parallel Algorithm for Nash Equilibrium in Multiplayer Stochastic Games with Application to Naval Sresults on a 4-player stochastic game motivated by a naval strategic planning scenario, showing that our algorithm is able to quickly compute strategies constituting Nash equilibrium up to a very small degree of approximation error.作者: VAN 時間: 2025-3-22 22:31
LAC-Nav: Collision-Free Multiagent Navigation Based on the Local Action Cells,, in which the performance of selected motions are evaluated and used as the references for making decisions in the following updates. The efficiency of the proposed approaches were demonstrated through the experiments for three commonly considered scenarios, where the comparisons have been made with several well studied strategies.作者: Interlocking 時間: 2025-3-23 03:37
https://doi.org/10.1007/978-3-319-24237-8lated robotics environments show that our method enables more efficient and generalized meta-learning from past experience and outperforms state-of-the-art meta-RL and Hierarchical-RL methods in sparse reward settings.作者: 功多汁水 時間: 2025-3-23 05:47 作者: 通情達理 時間: 2025-3-23 11:35 作者: Atmosphere 時間: 2025-3-23 14:33 作者: 單調(diào)性 時間: 2025-3-23 18:07 作者: 彎曲的人 時間: 2025-3-23 23:30
D3PG: Decomposed Deep Deterministic Policy Gradient for Continuous Control,used deep reinforcement learning algorithms. Another advantage of D3PG is that it is able to provide explicit interpretations of the final learned policy as well as the underlying dependencies among the joints of a learning robot.作者: Armory 時間: 2025-3-24 05:13
Efficient Exploration by Novelty-Pursuit,ironments from simple maze environments, MuJoCo tasks, to the long-horizon video game of SuperMarioBros. Experiment results show that the proposed method outperforms the state-of-the-art approaches that use curiosity-driven exploration.作者: 運動吧 時間: 2025-3-24 08:19
Battery Management for Automated Warehouses via Deep Reinforcement Learning,ssian noise to enforce exploration could perform poorly in the formulated MDP, and present a novel algorithm called TD3-ARL that performs effective exploration by regulating the magnitude of the outputted action. Finally, extensive empirical evaluations confirm the superiority of our algorithm over the state-of-the-art and the rule-based policies.作者: 生銹 時間: 2025-3-24 11:35
Parallel Algorithm for Nash Equilibrium in Multiplayer Stochastic Games with Application to Naval Such settings can be modeled as stochastic games. While algorithms have been developed for solving (i.e., computing a game-theoretic solution concept such as Nash equilibrium) two-player zero-sum stochastic games, research on algorithms for non-zero-sum and multiplayer stochastic games is limited. We作者: 粗糙 時間: 2025-3-24 16:28
LAC-Nav: Collision-Free Multiagent Navigation Based on the Local Action Cells,ets, attentions should be paid to avoid the collisions with the others. In this paper, we introduced the concept of the ., which provides for each agent a set of velocities that are safe to perform. Consequently, as long as the local action cells are updated on time and each agent selects its motion作者: medieval 時間: 2025-3-24 20:43
MGHRL: Meta Goal-Generation for Hierarchical Reinforcement Learning, space. Such algorithms work well in tasks with relatively slight differences. However, when the task distribution becomes wider, it would be quite inefficient to directly learn such a meta-policy. In this paper, we propose a new meta-RL algorithm called Meta Goal-generation for Hierarchical RL (MGH作者: 施魔法 時間: 2025-3-25 02:09
D3PG: Decomposed Deep Deterministic Policy Gradient for Continuous Control,gh dimensional robotic control problems. In this regard, we propose the D3PG approach, which is a multiagent extension of DDPG by decomposing the global critic into a weighted sum of local critics. Each of these critics is modeled as an individual learning agent that governs the decision making of a作者: lattice 時間: 2025-3-25 07:21 作者: 侵略主義 時間: 2025-3-25 08:34 作者: Constant 時間: 2025-3-25 13:41
Efficient Exploration by Novelty-Pursuit,is issue include the intrinsically motivated goal exploration processes (IMGEP) and the maximum state entropy exploration (MSEE). In this paper, we propose a goal-selection criterion in IMGEP based on the principle of MSEE, which results in the new exploration method .. Novelty-pursuit performs the 作者: Irrigate 時間: 2025-3-25 17:44
Context-Aware Multi-agent Coordination with Loose Couplings and Repeated Interaction,g due to its combinatorial nature. First, with an exponentially scaling action set, it is challenging to search effectively and find the right balance between exploration and exploitation. Second, performing maximization over all agents’ actions jointly is computationally intractable. To tackle thes作者: cliche 時間: 2025-3-25 23:15 作者: phlegm 時間: 2025-3-26 00:18
The Eastern Arctic Seas Encyclopediarous behaviors in real applications. Hence, without stability guarantee, the application of the existing MARL algorithms to real multi-agent systems is of great concern, e.g., UAVs, robots, and power systems, etc. In this paper, we aim to propose a new MARL algorithm for decentralized multi-agent co作者: 安定 時間: 2025-3-26 06:10
Finding a Way Forward for Free Trade stability of the learning, and is able to deal robustly with overgeneralization, miscoordination, and high degree of stochasticity in the reward and transition functions. Our method outperforms state-of-the-art multi-agent learning algorithms across a spectrum of stochastic and partially observable作者: 評論性 時間: 2025-3-26 11:44
The Rise of Chinese Multinationalsming technique to improve the context exploitation process and a variable elimination technique to efficiently perform the maximization through exploiting the loose couplings. Third, two enhancements to MACUCB are proposed with improved theoretical guarantees. Fourth, we derive theoretical bounds on作者: Mingle 時間: 2025-3-26 13:44 作者: Palter 時間: 2025-3-26 17:17
Hybrid Independent Learning in Cooperative Markov Games, stability of the learning, and is able to deal robustly with overgeneralization, miscoordination, and high degree of stochasticity in the reward and transition functions. Our method outperforms state-of-the-art multi-agent learning algorithms across a spectrum of stochastic and partially observable作者: 搖曳的微光 時間: 2025-3-26 21:02
Context-Aware Multi-agent Coordination with Loose Couplings and Repeated Interaction,ming technique to improve the context exploitation process and a variable elimination technique to efficiently perform the maximization through exploiting the loose couplings. Third, two enhancements to MACUCB are proposed with improved theoretical guarantees. Fourth, we derive theoretical bounds on作者: 無情 時間: 2025-3-27 02:32 作者: 抱怨 時間: 2025-3-27 07:23
978-3-030-64095-8Springer Nature Switzerland AG 2020作者: 衍生 時間: 2025-3-27 10:17 作者: 乳白光 時間: 2025-3-27 16:24 作者: inchoate 時間: 2025-3-27 19:42
https://doi.org/10.1007/978-3-319-24237-8 space. Such algorithms work well in tasks with relatively slight differences. However, when the task distribution becomes wider, it would be quite inefficient to directly learn such a meta-policy. In this paper, we propose a new meta-RL algorithm called Meta Goal-generation for Hierarchical RL (MGH作者: 果仁 時間: 2025-3-27 23:33
Alaska-Siberian Air Road, “ALSIB”gh dimensional robotic control problems. In this regard, we propose the D3PG approach, which is a multiagent extension of DDPG by decomposing the global critic into a weighted sum of local critics. Each of these critics is modeled as an individual learning agent that governs the decision making of a作者: Tincture 時間: 2025-3-28 03:50
The Eastern Arctic Seas Encyclopediaagent control, systems are complex with unknown or highly uncertain dynamics, where traditional model-based control methods can hardly be applied. Compared with model-based control in control theory, deep reinforcement learning (DRL) is promising to learn the controller/policy from data without the 作者: 上下倒置 時間: 2025-3-28 08:41
Finding a Way Forward for Free Tradeization. An independent learner may receive different rewards for the same state and action at different time steps, depending on the actions of the other agents in that state. Existing multi-agent learning methods try to overcome these issues by using various techniques, such as hysteresis or lenie作者: gain631 時間: 2025-3-28 13:49
Education, Talent, and Cultural Tiesis issue include the intrinsically motivated goal exploration processes (IMGEP) and the maximum state entropy exploration (MSEE). In this paper, we propose a goal-selection criterion in IMGEP based on the principle of MSEE, which results in the new exploration method .. Novelty-pursuit performs the 作者: Palatial 時間: 2025-3-28 16:42 作者: aggrieve 時間: 2025-3-28 20:29
https://doi.org/10.1007/978-3-031-59242-3t capacity. In an automated warehouse, orders are fulfilled by battery-powered AGVs transporting movable shelves or boxes. Therefore, battery management is crucial to the productivity since recovering depleted batteries can be time-consuming and seriously affect the overall performance of the system作者: 破裂 時間: 2025-3-29 01:32
Distributed Artificial Intelligence978-3-030-64096-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: insomnia 時間: 2025-3-29 07:01
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/281740.jpg作者: 全等 時間: 2025-3-29 07:55
