Add to ORKGAbstract This paper presents work on using hierarchical long term memory to reduce the memory requirements of nearest sequence memory (NSM) learning, a previously published, instance-based reinforcement learning algorithm. A hierar-chical memory representation reduces the memory requirements by allowing traces to share common sub-sequences. We present moderated mechanisms for estimat-ing discounted future rewards and for dealing with hidden state using hierarchical memory. We also present an experimental analysis of how the sub-sequence length affects the memory compression achieved and show that the reduced memory re-quirements do not effect the speed of learning. Finally, we analyse and discuss the persistence of the sub-sequences indepen...
Hierarchical Reinforcement Learning (HRL) algorithms can perform planning at multiple levels of abst...
\u3cp\u3eThis paper proposes and experimentally assesses a machine learning approach for supporting ...
This work explores the capabilities of the current Reinforcement Learning algorithms and the Memory ...
In this study learning reinforcement and noise rejection of a spatial pooler was examined, the first...
The paper presents an approach for hierarchical reinforcement learning that does not rely on a prior...
Hierarchical Temporal Memory (HTM) is a biologically-inspired framework that can be used to learn in...
Common approaches to Reinforcement Learning (RL) are seriously challenged by large-scale application...
Do you want your neural net algorithm to learn sequences? Do not lim-it yourself to conventional gra...
A new learning algorithm for the hierarchical structure learning automata (HSLA) operating in the no...
Abstract. Reinforcement learning is bedeviled by the curse of dimensionality: the number of paramete...
The Memory-Prediction Framework (MPF) and its Hierarchical-Temporal Memory implementation (HTM) have...
The Memory-Prediction Framework (MPF) and its Hierarchical-Temporal Memory implementation (HTM) have...
Reward machines (RMs) are a recent formalism for representing the reward function of a reinforcement...
Reinforcement Learning (RL) algorithms allow artificial agents to improve their action selection pol...
Learning, has been very successful at describing how animals and humans adjust their actions so as t...
Hierarchical Reinforcement Learning (HRL) algorithms can perform planning at multiple levels of abst...
\u3cp\u3eThis paper proposes and experimentally assesses a machine learning approach for supporting ...
This work explores the capabilities of the current Reinforcement Learning algorithms and the Memory ...
In this study learning reinforcement and noise rejection of a spatial pooler was examined, the first...
The paper presents an approach for hierarchical reinforcement learning that does not rely on a prior...
Hierarchical Temporal Memory (HTM) is a biologically-inspired framework that can be used to learn in...
Common approaches to Reinforcement Learning (RL) are seriously challenged by large-scale application...
Do you want your neural net algorithm to learn sequences? Do not lim-it yourself to conventional gra...
A new learning algorithm for the hierarchical structure learning automata (HSLA) operating in the no...
Abstract. Reinforcement learning is bedeviled by the curse of dimensionality: the number of paramete...
The Memory-Prediction Framework (MPF) and its Hierarchical-Temporal Memory implementation (HTM) have...
The Memory-Prediction Framework (MPF) and its Hierarchical-Temporal Memory implementation (HTM) have...
Reward machines (RMs) are a recent formalism for representing the reward function of a reinforcement...
Reinforcement Learning (RL) algorithms allow artificial agents to improve their action selection pol...
Learning, has been very successful at describing how animals and humans adjust their actions so as t...
Hierarchical Reinforcement Learning (HRL) algorithms can perform planning at multiple levels of abst...
\u3cp\u3eThis paper proposes and experimentally assesses a machine learning approach for supporting ...
This work explores the capabilities of the current Reinforcement Learning algorithms and the Memory ...