Human ability at solving complex tasks is helped by priors on object and event semantics of their environment. This paper investigates the use of similar prior knowledge for transfer learning in Reinforcement Learning agents. In particular, the paper proposes to use a first-order-logic language grounded in deep neural networks to represent facts about objects and their semantics in the real world. Facts are provided as background knowledge a priori to learning a policy for how to act in the world. The priors are injected with the conventional input in a single agent architecture. As proof-of-concept, the paper tests the system in simple experiments that show the importance of symbolic abstraction and flexible fact derivation. The paper shows that the proposed system can learn to take advantage of both the symbolic layer and the image layer in a single decision selection module.
The combination of Deep Neural Networks and Reinforcement Learning has been the most successful approach to Reinforcement Learning in the last ten years. Most famously DRL has solved ATARI games @cite_1 and the game of Go @cite_2 . ATARI and Go both are essentially discrete state and action space Markov Decision Problems. But, DRL has also been applied to continuous control problems with multiple proposals existing for continuous state and action spaces @cite_0 @cite_6 @cite_9 . In principle our approach is compatible with all discrete time Reinforcement Learning problems. Our approach combines rich (continuous) input from images or other sensori information with symbolic information and we then apply any DRL learning system. Our overall system differs from pure DRL systems by being able to easily incorporate prior and background knowledge available in a first-order grounded language.
@cite_9 @cite_1 @cite_6 @cite_0 @cite_2
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Motivated by vision-based reinforcement learning (RL) problems, in particular Atari games from the recent benchmark Aracade Learning Environment (ALE), we consider spatio-temporal prediction problems where future image-frames depend on control variables or actions as well as previous frames. While not composed of natural scenes, frames in Atari games are high-dimensional in size, can involve tens of objects with one or more objects being controlled by the actions directly and many other objects being influenced indirectly, can involve entry and departure of objects, and can involve deep partial observability. We propose and evaluate two deep neural network architectures that consist of encoding, action-conditional transformation, and decoding layers based on convolutional neural networks and recurrent neural networks. Experimental results show that the proposed architectures are able to generate visually-realistic frames that are also useful for control over approximately 100-step action-conditional futures in some games. To the best of our knowledge, this paper is the first to make and evaluate long-term predictions on high-dimensional video conditioned by control inputs. Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the generic task of trial-and-error learning, and its effectiveness has been convincingly demonstrated on tasks such as Atari video games and the game of Go. However, contemporary DRL systems inherit a number of shortcomings from the current generation of deep learning techniques. For example, they require very large datasets to work effectively, entailing that they are slow to learn even when such datasets are available. Moreover, they lack the ability to reason on an abstract level, which makes it difficult to implement high-level cognitive functions such as transfer learning, analogical reasoning, and hypothesis-based reasoning. Finally, their operation is largely opaque to humans, rendering them unsuitable for domains in which verifiability is important. In this paper, we propose an end-to-end reinforcement learning architecture comprising a neural back end and a symbolic front end with the potential to overcome each of these shortcomings. As proof-of-concept, we present a preliminary implementation of the architecture and apply it to several variants of a simple video game. We show that the resulting system -- though just a prototype -- learns effectively, and, by acquiring a set of symbolic rules that are easily comprehensible to humans, dramatically outperforms a conventional, fully neural DRL system on a stochastic variant of the game. Motivated by vision-based reinforcement learning (RL) problems, in particular Atari games from the recent benchmark Aracade Learning Environment (ALE), we consider spatio-temporal prediction problems where future (image-)frames are dependent on control variables or actions as well as previous frames. While not composed of natural scenes, frames in Atari games are high-dimensional in size, can involve tens of objects with one or more objects being controlled by the actions directly and many other objects being influenced indirectly, can involve entry and departure of objects, and can involve deep partial observability. We propose and evaluate two deep neural network architectures that consist of encoding, action-conditional transformation, and decoding layers based on convolutional neural networks and recurrent neural networks. Experimental results show that the proposed architectures are able to generate visually-realistic frames that are also useful for control over approximately 100-step action-conditional futures in some games. To the best of our knowledge, this paper is the first to make and evaluate long-term predictions on high-dimensional video conditioned by control inputs. Driven by recent developments in the area of Artificial Intelligence research, a promising new technology for building intelligent agents has evolved. The technology is termed Deep Reinforcement Learning (DRL) and combines the classic field of Reinforcement Learning (RL) with the representational power of modern Deep Learning approaches. DRL enables solutions for difficult and high dimensional tasks, such as Atari game playing, for which previously proposed RL methods were inadequate. However, these new solution approaches still take a long time to learn how to actuate in such domains and so far are mainly researched for single task scenarios. The ability to generalize gathered knowledge and transfer it to another task has been researched for classical RL, but remains an open problem for the DRL domain. Consequently, in this article we evaluate under which conditions the application of Transfer Learning (TL) to the DRL domain improves the learning of a new task. Our results indicate that TL can greatly accelerate DRL when transferring knowledge from similar tasks, and that the similarity between tasks plays a key role in the success or failure of knowledge transfer.