Towards a General Theory of Moral Learning, Planning, and Decision: Algorithmic and Psychological Measures


Towards a General Theory of Moral Learning, Planning, and Decision: Algorithmic and Psychological Measures – This paper presents a framework for learning to reason and performing reasoning based on a computational model of action plans from a set of simulation simulations. The framework allows the human to perform a logical analysis of a real-world scenario, which is then used to obtain a set of actions. Our framework is based on using a set of action plans generated from an action policy. Then our framework is implemented.

We present a multi-task learning approach for reinforcement-based learning, in which agents use the output of their own reasoning mechanisms towards solving a problem that they have solved in isolation. Such a system is able to learn from the input sequence in a non-monotonic manner, whereas existing multi-task learning approaches only rely on the solution of a single task to infer the output. However, we develop a reinforcement learning approach that learns not only from the inputs but also the solutions of multiple tasks. Furthermore, we demonstrate the method’s potential in a simulation environment where two agents play an Atari game in which the players do not know which actions are the appropriate ones.

Research in policy-based reinforcement learning shows promise in finding useful policies with the goal of making policy updates. This work proposes to develop a novel reinforcement learning policy search algorithm that is suitable for an unknown task. The new policy search algorithm is shown to be simple to implement by using deep reinforcement learning. In addition, the performance of the proposed policy search algorithm is demonstrated on simulated and real world real tasks with a variety of behaviors. The simulation results demonstrate that the proposed policy search algorithm performs well compared with state-of-the-art policies based on reinforcement learning.

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Towards a General Theory of Moral Learning, Planning, and Decision: Algorithmic and Psychological Measures

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  • Single-Shot Recognition with Deep Priors

    Efficient Policy Search for Reinforcement LearningResearch in policy-based reinforcement learning shows promise in finding useful policies with the goal of making policy updates. This work proposes to develop a novel reinforcement learning policy search algorithm that is suitable for an unknown task. The new policy search algorithm is shown to be simple to implement by using deep reinforcement learning. In addition, the performance of the proposed policy search algorithm is demonstrated on simulated and real world real tasks with a variety of behaviors. The simulation results demonstrate that the proposed policy search algorithm performs well compared with state-of-the-art policies based on reinforcement learning.


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