Reinforcement Learning, adaptive agents and SOAR
Hierarchical reinforcement learning for trading agents
... A reinforcement learning technique, called the Monte Carlo approach and introduced in Section ...the reinforcement learning to solve this decision problem are presented Sections ...
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Reinforcement learning with motivations for realistic agents
... Realistic Agents Large story driven game genres, such as role playing games, and action-adventure games, consist of many individual characters that are important to the overall player ...
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Hierarchical Reinforcement Learning for Adaptive Text Generation
... 5.2 Comparison of learnt and baseline policies In order to test our framework, we designed a sim- ulated environment that simulates different naviga- tional situations, routes of different lengths and dif- ferent user ...
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Reinforcement Learning for the Adaptive Scheduling of Educational Activities
... ABSTRACT Adaptive instruction for online education can increase learn- ing gains and decrease the work required of learners, instruc- tors, and course ...designers. Reinforcement Learning (RL) is a ...
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Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA)
... Cooperative Reinforcement Learning by Expert Agents (MCRLEA) for dynamic decision making in the retail ...cooperative reinforcement learning i.e. EQ learning, EGroup, EDynamic, ...
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Vector Quantization for Adaptive State Aggregation in Reinforcement Learning*
... for Adaptive State Aggregation in Reinforcement Learning* Christos ...an adaptive state aggregation scheme to be used along with temporal-difference reinforcement learn- ing and value ...
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Adaptive Streaming of 360-Degree Videos with Reinforcement Learning
... for adaptive stream- ing of 360-degree ...deep reinforcement learning, and they can dy- namically determine which tiles to download at what qual- ities and when, depending on the network ...
Sequential Decision Task by Adaptive Reinforcement Learning Method
... the reinforcement learning ...as adaptive or decision model. Practical importance of adaptive method is; if this adaptive method can able to make improvement in decision policy ...
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A model of learning and emulation with artificial adaptive agents
... trial—and-error learning through which older, successful ideas arc retained and propagated while some new ideas are developed and exchanged among agents with heterogeneous beliefs and planning horizons, all ...
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RLBOA: A modular reinforcement learning framework for autonomous negotiating agents
... creating agents that negotiate in more realistic ...dependent agents, where the policy performs two standard deviations above the RandomAgent in every evaluated ...
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Reinforcement learning for trading dialogue agents in non-cooperative negotiations
... the experiments of the previous chapter were re-conducted using the new SARSA(λ) learning agent, along with others. In particular, the first experiment (Section 5.1) uses again the rule-based adversary (which is ...
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Building an Artificial Stock Market Populated by Reinforcement-Learning Agents
... ild in g a n A rti fic ia l S to ck M ar ke t p op ula te d b y R ein fo rce m en t-le ar nin g A ge nt s / T . R am an au sk as an d A . V . R utk au sk as In this paper we develop an artificial stock market (ASM) ...
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Understanding Language Evolution in Overlapping Generations of Reinforcement Learning Agents
... language learning and language change are influenced by the population structure of language users is crucial to understanding how lexical items and grammatical rules become established within the con- text of the ...
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Investigation into the effect of social learning in reinforcement learning board game playing agents
... Figure 10: Cross to Play (Easy) These are 1 move to win boards. They are relatively easy and test how the agents try to choose actions that will maximise reward in their next action choice. The intermediate boards ...
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Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards
... a reinforcement learning (RL) approach for keyphrase generation, with an adaptive reward function that encourages a model to generate both sufficient and accu- rate ...
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Adaptive Policy-based Object Tracking using Reinforcement Learning.
... According to the model-construction mechanism, object tracking can also be categorized as a generative model-based, discriminative model-based, or hybrid generative-discriminative based. Interestingly, these two ...
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Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning
... model-based reinforcement learning (MBRL) [3, ...MBRL agents must learn this transition model from finite experience, which induces approximation ...model-based reinforcement learning, ...
Maximizing Throughput using Adaptive Routing Based on Reinforcement Learning
... Haenggi, “ Opportunities and Challenges in Wireless Sensor Networks,” in Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems, M. He, RAP: A Real-[r] ...
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Adaptive value function approximation in reinforcement learning using wavelets
... to learning, and full feature dependence is assumed between the state dimensions (where each dimen- sion corresponds to a different measurable quantity in the environment or ...to agents which do not scale ...
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Design of an FX trading system using Adaptive Reinforcement Learning
... • The parameters that govern the learning behaviour and influence the risk profile were optimized by maximizing a utility function at regular points in time (hence Adaptive RL). • The [r] ...
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