Dayan, Improving generalization for temporal difference learning: The successor representation, Neural Computation, vol. Neural Comput 5:613624.

Moving outside the temporal difference learning framework, it is also possible to learn the successor representation using biologically plausible plasticity rules, as shown by Brea et al., (2016). Appropriate generalization between states is determined by how similar their successors are, and representations should follow suit. Try to divide the integer n by every prime number.

"Improving generalization for temporal difference learning: The successor representation." Introduction by the Workshop Organizers; Jing Xiang Toh, Xuejie Zhang, Kay Jan Wong, Samarth Agarwal and John Lu Improving Operation Efficieny through Predicting Credit Card Application Turnaround Time with Index-based Encoding; Naoto Minakawa, Kiyoshi Izumi, Hiroki Sakaji and Hitomi Sano Graph Representation Learning of Banking Transaction Network with Edge In real-world settings like robotics for unstructured and dynamic environments, it is infeasible to model all meaningful aspects of a system and its environment by hand due to both complexity and size. 5(4), 613624 (1993). Dayan, P. Improving generalization for temporal difference learning: The successor representation. Google Scholar Morimoto and Atkeson, 2009 Morimoto J. , Atkeson G. , Nonparametric representation of an approximated poincare map for learning biped locomotion , Autonomous Robots 27 ( 2 ) ( 2009 ) 131 144 . This paper shows how TD machinery can be used to learn Abstract.

613 - 624 CrossRef View Record in Dayan, P (1993) Improving generalization for temporal difference learning: The successor representation. As a model-free learning agent only stores the value estimates of all states in memory, it needs to relearn value using slow, local updates. Our main contribution is to show that a variant of the temporal context model (TCM; Howard & Kahana, 2002), an inuential model of episodic memory, can be understood as directly estimating the successor representation using the temporal difference learning algorithm (Sutton & Barto, 1998). A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation. Luo, Yuping, et al. However, this discretization of space M~ and ~R can be learnt online using temporal-difference learning rules: Dayan, P. (1993). The SR M encapsulates both the short- and long-term state-transition dynamics of the environment, with a time-horizon dictated by the discount parameter . It leverages the insight that the same type of recurrence relation used to train $$Q$$-functions: \[ Q(\mathbf{s}_t, \mathbf{a}_t) \leftarrow \mathbb{E}_{\mathbf{s}_{t+1}} Learning successor features is a form of temporal difference learning and is equivalent to learning to predict a single policy's utility, which is a characteristic of model-free agents. Estimation of returns over time, the focus of temporal difference (TD) algorithms, imposes particular constraints on good function approximators or representations. 1137 Projects 1137 incoming 1137 knowledgeable 1137 meanings 1137 1136 demonstrations 1136 escaped 1136 notification 1136 FAIR 1136 Hmm 1136 CrossRef 1135 arrange 1135 LP 1135 forty 1135 suburban 1135 GW 1135 herein 1135 intriguing 1134 Move 1134 Reynolds 1134 positioned 1134 didnt 1134 int 1133 Chamber 1133 termination 1133 overlapping 1132 newborn Improving Generalization for Temporal Difference Learning: The Successor Representation. Dayan, P. Improving Generalization for Temporal Differ-ence Learning: The Successor Representation. a variant of temporal difference learning that uses this richer form of eligibility traces, an algorithm we call Predecessor Representation.

SRSA quantifies regularities in scan patterns using temporal-difference learning to construct a fixed-size matrix called a successor representation (SR, ). Perceptual tasks such as object matching, mammogram interpretation, mental rotation, and satellite imagery change detection often require the assignment of correspondences to fuse information across views. "Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees." US One Rogers Street Cambridge, MA 02142-1209. [1] Dayan, Peter. Google Scholar | Crossref By Content Type. Improving generalization for temporal difference learning: The successor representation. This paper shows how TD machinery can be used to We are not allowed to display external PDFs yet. Instead, Successor Feature Neural Episodic Control. Improving Generalization for Temporal Difference Learning: The Successor Representation By Peter Dayan Get PDF (848 KB)

The successor representation (SR) is a candidate principle for generalization in reinforcement learning, computational accounts of memory, and the structure of neural representations in the hippocampus. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Temporal-difference (TD) learning can be used not just to predict rewards, as is commonly done in reinforcement learning, but also to predict states, i.e., to learn a model of the world's dynamics. In real-world set- We present theory and algorithms for intermixing TD models of the world at different levels of Journal of Machine Learning Research, 15:809883, 2014. Dayan P. (1993) Improving generalization for temporal difference learning: the successor representation. Google Scholar 5, No. Preferential Temporal Difference Learning. model is learned using a recurrent architecture so the latent representation can incorporate temporal dependencies, where h We rst consider the Simple Generalization case (Table 12). MIT Press. A longstanding goal in reinforcement learning is to build intelligent agents that show fast learning and a flexible transfer of skills akin to humans and animals. This allows new value functions to be evaluated with a smaller Here we propose using the successor representation (SR) to accelerate learning in a constructive knowledge system based on general value functions (GVFs). Neural Comput. Dayan, Peter. In Advances in Neural Information Processing Systems, volume 5, pages 271278, Cambridge, MA, 1993. Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. 5, 613624 (1993). The specific way we do so is through a generalization of two fundamental operations in reinforcement learning: policy improvement and policy evaluation. Right now it is just a list; if I have time Ill add summaries for those papers Ive read. Improving Generalization for Temporal Difference Learning: The Successor Representation By Peter Dayan Get PDF (848 KB) The objective of transfer reinforcement learning is to generalize from a set of previous tasks to unseen new tasks. David Emukpere. Estimation of returns over time, the focus of temporal difference (TD) algorithms, imposes particular constraints on good function approximators or representations. This paper shows how TD machinery can be used to Google Scholar; Dayan, P. 1993. Appropriate generalization between states is determined by how similar their successors are, and representations should follow suit. somewhat predictive and can be modeled b y learning a successor representation (SR) between distinct positions in an environm ent. Neural Computation, 5:613624, 1993. Trial division. "Successor features for transfer in reinforcement learning." This allows new value functions to be evaluated with a smaller It is not exhaustive and I have not read it all. Neural Computation, 5(4):613624, 1993. Improving Generalization for Temporal Difference Learning: The Successor Representation. Dayan, P (1993) Improving generalization for temporal difference learning: The successor representation.