This document summarizes a research paper on inverse constrained reinforcement learning. The paper proposes a method to estimate cost functions from expert data in continuous action spaces to achieve optimal behavior under constraints. It formulates cost function inference as a maximum entropy inverse reinforcement learning model and uses a neural network to approximate the cost function. The method employs importance sampling and early stopping to improve learning efficiency. Evaluation results demonstrate the method outperforms alternatives in terms of cumulative reward and constraint violations, and the learned cost functions can be effectively transferred to new tasks.
【DL輪読会】Incorporating group update for speech enhancement based on convolutio...Deep Learning JP
1. The document discusses a research paper on speech enhancement using a convolutional gated recurrent network (CGRN) and ordered neuron long short-term memory (ON-LSTM).
2. The proposed method aims to improve speech quality by incorporating both time and frequency dependencies using CGRN, and handling noise with varying change rates using ON-LSTM.
3. CGRN replaces fully-connected layers with convolutions, allowing it to capture local spatial structures in the frequency domain. ON-LSTM groups neurons based on the change rate of internal information to model hierarchical representations.
The document summarizes recent research related to "theory of mind" in multi-agent reinforcement learning. It discusses three papers that propose methods for agents to infer the intentions of other agents by applying concepts from theory of mind:
1. The papers propose that in multi-agent reinforcement learning, being able to understand the intentions of other agents could help with cooperation and increase success rates.
2. The methods aim to estimate the intentions of other agents by modeling their beliefs and private information, using ideas from theory of mind in cognitive science. This involves inferring information about other agents that is not directly observable.
3. Bayesian inference is often used to reason about the beliefs, goals and private information of other agents based
The document summarizes a research paper that compares the performance of MLP-based models to Transformer-based models on various natural language processing and computer vision tasks. The key points are:
1. Gated MLP (gMLP) architectures can achieve performance comparable to Transformers on most tasks, demonstrating that attention mechanisms may not be strictly necessary.
2. However, attention still provides benefits for some NLP tasks, as models combining gMLP and attention outperformed pure gMLP models on certain benchmarks.
3. For computer vision, gMLP achieved results close to Vision Transformers and CNNs on image classification, indicating gMLP can match their data efficiency.
- The document introduces Deep Counterfactual Regret Minimization (Deep CFR), a new algorithm proposed by Noam Brown et al. in ICML 2019 that incorporates deep neural networks into Counterfactual Regret Minimization (CFR) for solving large imperfect-information games.
- CFR is an algorithm for computing Nash equilibria in two-player zero-sum games by minimizing cumulative counterfactual regret. It scales poorly to very large games that require abstraction of the game tree.
- Deep CFR removes the need for abstraction by using a neural network to generalize the strategy across the game tree, allowing it to solve previously intractable games like no-limit poker.
The document summarizes recent research related to "theory of mind" in multi-agent reinforcement learning. It discusses three papers that propose methods for agents to infer the intentions of other agents by applying concepts from theory of mind:
1. The papers propose that in multi-agent reinforcement learning, being able to understand the intentions of other agents could help with cooperation and increase success rates.
2. The methods aim to estimate the intentions of other agents by modeling their beliefs and private information, using ideas from theory of mind in cognitive science. This involves inferring information about other agents that is not directly observable.
3. Bayesian inference is often used to reason about the beliefs, goals and private information of other agents based
The document summarizes a research paper that compares the performance of MLP-based models to Transformer-based models on various natural language processing and computer vision tasks. The key points are:
1. Gated MLP (gMLP) architectures can achieve performance comparable to Transformers on most tasks, demonstrating that attention mechanisms may not be strictly necessary.
2. However, attention still provides benefits for some NLP tasks, as models combining gMLP and attention outperformed pure gMLP models on certain benchmarks.
3. For computer vision, gMLP achieved results close to Vision Transformers and CNNs on image classification, indicating gMLP can match their data efficiency.
- The document introduces Deep Counterfactual Regret Minimization (Deep CFR), a new algorithm proposed by Noam Brown et al. in ICML 2019 that incorporates deep neural networks into Counterfactual Regret Minimization (CFR) for solving large imperfect-information games.
- CFR is an algorithm for computing Nash equilibria in two-player zero-sum games by minimizing cumulative counterfactual regret. It scales poorly to very large games that require abstraction of the game tree.
- Deep CFR removes the need for abstraction by using a neural network to generalize the strategy across the game tree, allowing it to solve previously intractable games like no-limit poker.
76. 学ぶべきことは死ぬまで尽きない
Tomoaki
Nishikawa,
2016
76
Learn as if you will live forever.
Live as if you will die tomorrow.
永遠に⽣生きるかのように学び、明⽇日死ぬかのように⽣生きよ。
[マハトマ・ガンジー]
受験勉強は単に「志望校へ⼊入学するのに必要な作業」と捉えることもできる。
しかし「⽬目的に向けて効率率率的かつ⾃自発的に学習する訓練」とも捉えられる。
ひとりの⼈人間に世界は広⼤大で、社会は複雑で、ゆえに学ぶべきことは尽きない。
⼦子どもの受験勉強が単なる作業ではなく未来に活きる習慣の獲得過程となるこ
とを願って⽌止まないし、このドキュメントが少しでもそれに寄与できたなら、
筆者としての幸甚これに勝るものはないと確信する。