Training a Reinforcement Learning Agent based on XCS in a Competitive Snake Environment

Büttner, Johannes; Mammen, Sebastian von

doi:10.1109/cog52621.2021.9619161

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Büttner and von Mammen [5] use XCS-RC, an XCS derivative with inductive reasoning rather than stochastic optimization, to play games of competitive snake. They focus on self-play-based RL, where two agents with the same model play against each other.…”

Section: Reinforcement Learningmentioning

confidence: 99%

An overview of LCS research from 2021 to 2022

Heider

Pätzel

Wagner

2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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The International Workshop on Learning Classifier Systems (IWLCS) is an annual workshop at the GECCO conference where new concepts and results regarding learning classifier systems (LCSs) are presented and discussed. One recurring part of the workshop agenda is a presentation that reviews and summarizes the advances made in the field over the last year; this is intended to provide an easy entry point to the most recent progress and achievements. The 2020 and 2021 presentations were accompanied by survey workshop papers, a practice which we hereby continue. We give an overview of all the LCS-related publications from 11 March 2021 to 10 March 2022. The 42 publications we review are grouped into six overall topics: Formal theoretic advances, new LCS architectures, LCS-based reinforcement learning, algorithmic improvements to existing LCSs, combinations of LCS and Deep Learning models and, finally, a variety of applications of LCSs. CCS CONCEPTS• Computing methodologies → Rule learning; Genetic algorithms; • General and reference → Surveys and overviews.

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Section: Reinforcement Learningmentioning

confidence: 99%

An overview of LCS research from 2021 to 2022

Heider

Pätzel

Wagner

2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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“…However, training a reinforcement learning model is time-consuming due to the massive interactions between the learning and the environment [3]- [5]. Also, interactions with naive exploration strategies slow down the model's learning speed and waste resources during model training [6], [7]. These deficiencies restrict the applications of reinforcement learning in games.…”

Section: Introductionmentioning

confidence: 99%

CGAR: Critic Guided Action Redistribution in Reinforcement Leaning

Huang¹,

Li²,

Li³

et al. 2022

Preprint

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Training a game-playing reinforcement learning agent requires multiple interactions with the environment. Ignorant random exploration may cause a waste of time and resources. It's essential to alleviate such waste. As discussed in this paper, under the settings of the off-policy actor critic algorithms, we demonstrate that the critic can bring more expected discounted rewards than or at least equal to the actor. Thus, the Q value predicted by the critic is a better signal to redistribute the action originally sampled from the policy distribution predicted by the actor. This paper introduces the novel Critic Guided Action Redistribution (CGAR) algorithm and tests it on the OpenAI MuJoCo tasks. The experimental results demonstrate that our method improves the sample efficiency and achieves state-of-the-art performance. Our code can be found at https://github.com/tairanhuang/CGAR.

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CGAR: Critic Guided Action Redistribution in Reinforcement Leaning

Huang

et al. 2022

2022 IEEE Conference on Games (CoG)

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Training a Reinforcement Learning Agent based on XCS in a Competitive Snake Environment

Cited by 3 publications

References 11 publications

An overview of LCS research from 2021 to 2022

An overview of LCS research from 2021 to 2022

CGAR: Critic Guided Action Redistribution in Reinforcement Leaning

CGAR: Critic Guided Action Redistribution in Reinforcement Leaning

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