这个系列后续还有Arslan and Y¨uksel (2016)[7]提出的 decentralised Q-learning algorithms,其结合了 two-timescale analysis (Leslie et al., 2003[8])方法,可以在弱非循环博弈中收敛到一个均衡策略。为了进一步避免弱非循环博弈中的次优均衡,Yongacoglu et al. (2019)[9]改进了 decentralised Q-learners,...
Thus, it is almost impossible to evaluate learning performance before the end of learning cycle. This study proposes a multi-agent framework to real-time monitor learners' behavior, forecast their learning performance, and adapt proper strategies by both lecturers and students to increase learning ...
learning task allocation strategy.Furthermore,It uses compensates mechanism to encourage the agent cooperation,state-space-search theory to enable the MAS system to have the stronger problem solving ability,both of which can meet demand of active learning for the learners and to some extent ...
multi-agent deep reinforcement learning (MADRL) has witnessed great achievements in recent years, where agents can process high-dimensional data and have generalization ability in large state and action spaces [7,8]. We notice that a large number of research works focus onlearning...
This paper proposes an approach for learning to coor- dinate verbal and non-verbal behaviours in interactive robots. It is based on a hierarchy of multiagent reinforcement learners executing verbal and non-verbal actions in parallel. Our approach is evaluated in a conversational humanoid robot that...
Many different value-based or policy-search reinforcement learning algorithms have been applied to multi-agent settings. Value-based learners estimate the expected return (value) for each state-action combination and then derive a policy from these expectations. Policy-search learners optimize the agent...
reward, action_fn: Callable = lambda action: action, name: str='IMAgent', q_network=None, # training params replay_buffer_max_length: int = 1000, learning_rate: float = 1e-5, training_batch_size: int = 8, training_parallel_calls: int = 3, training_prefetch_buffer_size: int = 3...
agent learning problems. The curse of dimensionality is also worse in a multiagent setting as every additional agent increases the state-action space. At the same time, MARL introduces a new set of opportunities as agents may share knowledge and imitate or directly learn from other learning ...
Recent years have witnessed significant advances in reinforcement learning (RL), which has registered tremendous success in solving various sequential decision-making problems in machine learning. Most of the successful RL applications, e.g., the games o
Claus and Boutilier (1998) distinguished between two types of learning, namely independent learners and joint-action learners. The former ignores the existence of other agents and cannot observe the rewards and selected actions of others as considered in Bowling and Veloso (2002) and Lauer and ...