12,353 research outputs found
Modeling Mutual Influence in Multi-Agent Reinforcement Learning
In multi-agent systems (MAS), agents rarely act in isolation but tend to achieve their goals through interactions with other agents. To be able to achieve their ultimate goals, individual agents should actively evaluate the impacts on themselves of other agents' behaviors before they decide which actions to take. The impacts are reciprocal, and it is of great interest to model the mutual influence of agent's impacts with one another when they are observing the environment or taking actions in the environment. In this thesis, assuming that the agents are aware of each other's existence and their potential impact on themselves, I develop novel multi-agent reinforcement learning (MARL) methods that can measure the mutual influence between agents to shape learning. The first part of this thesis outlines the framework of recursive reasoning in deep multi-agent reinforcement learning. I hypothesize that it is beneficial for each agent to consider how other agents react to their behavior. I start from Probabilistic Recursive Reasoning (PR2) using level-1 reasoning and adopt variational Bayes methods to approximate the opponents' conditional policies. Each agent shapes the individual Q-value by marginalizing the conditional policies in the joint Q-value and finding the best response to improving their policies. I further extend PR2 to Generalized Recursive Reasoning (GR2) with different hierarchical levels of rationality. GR2 enables agents to possess various levels of thinking ability, thereby allowing higher-level agents to best respond to less sophisticated learners. The first part of the thesis shows that eliminating the joint Q-value to an individual Q-value via explicitly recursive reasoning would benefit the learning. In the second part of the thesis, in reverse, I measure the mutual influence by approximating the joint Q-value based on the individual Q-values. I establish Q-DPP, an extension of the Determinantal Point Process (DPP) with partition constraints, and apply it to multi-agent learning as a function approximator for the centralized value function. An attractive property of using Q-DPP is that when it reaches the optimum value, it can offer a natural factorization of the centralized value function, representing both quality (maximizing reward) and diversity (different behaviors). In the third part of the thesis, I depart from the action-level mutual influence and build a policy-space meta-game to analyze agents' relationship between adaptive policies. I present a Multi-Agent Trust Region Learning (MATRL) algorithm that augments single-agent trust region policy optimization with a weak stable fixed point approximated by the policy-space meta-game. The algorithm aims to find a game-theoretic mechanism to adjust the policy optimization steps that force the learning of all agents toward the stable point
Multi-Agent Cooperation for Particle Accelerator Control
We present practical investigations in a real industrial controls environment
for justifying theoretical DAI (Distributed Artificial Intelligence) results,
and we discuss theoretical aspects of practical investigations for
accelerator control and operation. A generalized hypothesis is introduced,
based on a unified view of control, monitoring, diagnosis, maintenance and
repair tasks leading to a general method of cooperation for expert systems
by exchanging hypotheses. This has been tested for task and result sharing
cooperation scenarios. Generalized hypotheses also allow us to treat the
repetitive diagnosis-recovery cycle as task sharing cooperation. Problems
with such a loop or even recursive calls between the different agents are
discussed
The challenge of complexity for cognitive systems
Complex cognition addresses research on (a) high-level cognitive processes – mainly problem solving, reasoning, and decision making – and their interaction with more basic processes such as perception, learning, motivation and emotion and (b) cognitive processes which take place in a complex, typically dynamic, environment. Our focus is on AI systems and cognitive models dealing with complexity and on psychological findings which can inspire or challenge cognitive systems research. In this overview we first motivate why we have to go beyond models for rather simple cognitive processes and reductionist experiments. Afterwards, we give a characterization of complexity from our perspective. We introduce the triad of cognitive science methods – analytical, empirical, and engineering methods – which in our opinion have all to be utilized to tackle complex cognition. Afterwards we highlight three aspects of complex cognition – complex problem solving, dynamic decision making, and learning of concepts, skills and strategies. We conclude with some reflections about and challenges for future research
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