نتایج جستجو برای: dose reference level drl

تعداد نتایج: 1588802  

Journal: :CoRR 2018
Wenhao Yu Greg Turk C. Karen Liu

Learning locomotion skills is a challenging problem. To generate realistic and smooth locomotion, existing methods use motion capture, finite state machines or morphology-specific knowledge to guide the motion generation algorithms. Deep reinforcement learning (DRL) is a promising approach for the automatic creation of locomotion control. Indeed, a standard benchmark for DRL is to automatically...

Journal: :CoRR 2017
Gregory Palmer Karl Tuyls Daan Bloembergen Rahul Savani

Much of the success of single agent deep reinforcement learning (DRL) in recent years can be attributed to the use of experience replay memories (ERM), which allow Deep Q-Networks (DQNs) to be trained efficiently through sampling stored state transitions. However, care is required when using ERMs for multi-agent deep reinforcement learning (MA-DRL), as stored transitions can become outdated bec...

Journal: :CoRR 2018
Jingwei Zhang Lei Tai Yufeng Xiong Ming Liu Joschka Boedecker Wolfram Burgard

This paper deals with the reality gap from a novel perspective, targeting transferring Deep Reinforcement Learning (DRL) policies learned in simulated environments to the real-world domain for visual control tasks. Instead of adopting the common solutions to the problem by increasing the visual fidelity of synthetic images output from simulators during the training phase, this paper seeks to ta...

2011
Viola Cavallo Maria Pinto

Daytime Running Lights (DRL) on motorcycles have been shown to counteract the inherently lower sensory conspicuity of these vehicles and to significantly improve their safety. The advantage of the use of DRL exclusively by motorcycles is presently becoming lost by the increasing use of DRLs on cars. The present experiment aimed at evaluating the effects of car DRLs on motorcycle perception in a...

Journal: :CoRR 2017
Smruti Amarjyoti

The focus of this work is to enumerate the various approaches and algorithms that center around application of reinforcement learning in robotic manipulation tasks. Earlier methods utilized specialized policy representations and human demonstrations to constrict the policy. Such methods worked well with continuous state and policy space of robots but failed to come up with generalized policies....

Journal: :Pharmacology, biochemistry, and behavior 1977
J M Walsh L S Burch

Delta9-tetrahydrocannabinol in doses of 0.5, 1.0, 2.0 and 4.0 mg/kg was administered to rats under normal (1 ATA) and increased (3, 5 and 7 times normal) atmospheric pressure. Behavior was maintained by a food-reinforced differential-reinforcemnet-of-low-rate (DRL) schedule. Dose-dependent decrements in performance was observed at the 1 ATA conditions, in which response rates increased and the ...

2007
Ying Yao Yuping Wu Chong Yin Rie Ozawa Toshiro Aigaki Rene R Wouda Jasprina N Noordermeer Lee G Fradkin Huey Hing

Numerous studies have shown that ingrowing olfactory axons exert powerful inductive influences on olfactory map development. From an overexpression screen, we have identified wnt5 as a potent organizer of the olfactory map in Drosophila melanogaster. Loss of wnt5 resulted in severe derangement of the glomerular pattern, whereas overexpression of wnt5 resulted in the formation of ectopic midline...

2011
Janice D. Gobert Michael A. Sao Pedro Orlando Montalvo Ermal Toto Matthew Bachmann Ryan Shaun Joazeiro de Baker

Our learning environment Science Assistments (www.scienceassistments.org; NSF-DRL# 0733286; NSFDGE# 0742503; NSF-DRL# 1008649; U.S. Dept of Ed.# R305A090170) scaffolds middle school students’ scientific processes, namely, hypothesizing, design of experiments, data interpretation, warranting claims with evidence, and communicating findings for Physical Science, Life Science, and Earth Science. W...

Journal: :IEEE Trans. Signal Processing 1992
Shaw-Yin Lin Zen Chen

The design of a flexible parallel architecture for both the discrete relaxation labeling (DRL) algorithm and the probabilistic relaxation labeling (PRL) algorithm is addressed. Through the analysis of parallelism in the computational models of both algorithms, the parallel execution of the algorithms on a flexible parallel architecture i s presented. Three basic types of parallel operations are...

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