2014
DOI: 10.5430/air.v3n3p24
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Projective simulation applied to the grid-world and the mountain-car problem

Abstract: We study the model of projective simulation (PS) which is a novel approach to artificial intelligence (AI). Recently it was shown that the PS agent performs well in a number of simple task environments, also when compared to standard models of reinforcement learning (RL). In this paper we study the performance of the PS agent further in more complicated scenarios. To that end we chose two well-studied benchmarking problems, namely the "grid-world" and the "mountain-car" problem, which challenge the model with … Show more

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Cited by 17 publications
(35 citation statements)
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“…Due to this fact, this graph does not have a tree structure but rather resembles a maze. Navigating in a maze, in turn, constitutes one of the classic textbook RL problems [10,42,50,51]. Second, our empirical analysis suggests that experiments generating high-dimensional multipartite entanglement tend to have some structural similarities [12] (see Fig.…”
Section: Ingredients For Successful Learningmentioning
confidence: 83%
See 2 more Smart Citations
“…Due to this fact, this graph does not have a tree structure but rather resembles a maze. Navigating in a maze, in turn, constitutes one of the classic textbook RL problems [10,42,50,51]. Second, our empirical analysis suggests that experiments generating high-dimensional multipartite entanglement tend to have some structural similarities [12] (see Fig.…”
Section: Ingredients For Successful Learningmentioning
confidence: 83%
“…PS is a physics-motivated framework which can be used to construct RL agents. PS was shown to perform well in standard RL problems [41][42][43][44], in advanced robotics applications [45], and it is also amenable for quantum enhancements [46][47][48]. The main component of the PS agent is its memory network (shown in Fig.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…A special case of the RPS agents that we have considered in section 4 is obtained by considering the reflective analog of so-called 'two-layered' PS agents, where all transition are one-step transitions from percepts to actions [11]. Such agents have a very simple structure, yet were shown to be capable of learning to solve non-trivial environmental tasks [15,25]. In the RPS analog of two-layered PS agents [11], the associated Markov chains of each percept-specific clip network are rank-one throughout the entire learning process of the agent.…”
Section: A1 Rank-one Reflecting Psmentioning
confidence: 99%
“…This concept is inspired by occupancy grid maps [20], [21], as opposed to post-exploration maps [22] or topological maps [23]. For our cattle recovery task -and despite their simplicity -grid maps still represent a highly effective tool [19] for exploring the solution space of AI solutions [24], [25].…”
Section: Introduction and Related Workmentioning
confidence: 99%