Posts

[TOC] Title: On the Expressivity of Markov Reward Author: David Abel et. al. Publish Year: NuerIPS 2021 Review Date: 6 Dec 2021 Summary of paper This needs to be only 1-3 sentences, but it demonstrates that you understand the paper and, moreover, can summarize it more concisely than the author in his abstract. The author found out that in the Markov Decision Process scenario, (i.e., we do not look at the history of the trajectory to provide rewards), some tasks cannot be realised perfectly by reward functions. i.e., ...

[TOC] Title: Deep Reinforcement Learning at the Edge of the Statistical Precipice Author: Rishabh Agarwal et. al. Publish Year: NeurIPS 2021 Review Date: 3 Dec 2021 Summary of paper This needs to be only 1-3 sentences, but it demonstrates that you understand the paper and, moreover, can summarize it more concisely than the author in his abstract. Most current published results on deep RL benchmarks uses point estimate of aggregate performance such as mean and median score across the task. ...

[TOC] Title: Reward learning from human preferences and demonstractions in Atari Author: Borja Ibarz et. al. Publish Year: 2018 Review Date: Nov 2021 Summary of paper This needs to be only 1-3 sentences, but it demonstrates that you understand the paper and, moreover, can summarize it more concisely than the author in his abstract. The author proposed a method that uses human expert’s annotation rather than extrinsic reward from the environment to guide the reinforcement learning. ...

[TOC] Title: Go-Explore: a New Approach for Hard-Exploration Problems Author: Adrien Ecoffet et. al. Publish Year: 2021 Review Date: Nov 2021 Summary of paper This needs to be only 1-3 sentences, but it demonstrates that you understand the paper and, moreover, can summarize it more concisely than the author in his abstract. The author hypothesised that there are two main issues that prevent DRL agents from achieving high score in exploration-hard game (e.g., Montezuma’s Revenge) ...

[TOC] [论文简析]SAC: Soft Actor-Critic Part 1[1801.01290] hat means estimation

[TOC] Title: Agent57: Outperforming the Atari Human Benchmark 2020 Author: Adria Badia et. al. Publish Year: 2020 Review Date: Nov 2021 Summary of paper This needs to be only 1-3 sentences, but it demonstrates that you understand the paper and, moreover, can summarize it more concisely than the author in his abstract. Agent57 is the SOTA Atari RL agent in 2020 that can play difficult Atari games like “Montezuma’s Revenge, “Pitfall”, “Solaris” and “Skiing”. ...

[TOC] Title: Width-based Lookaheads with Learnt Base Policies and Heuristics Over the Atari-2600 Benchmark Author: Stefan O’Toole et. al. Publish Year: 2021 Review Date: Tue 16 Nov 2021 Summary of paper This needs to be only 1-3 sentences, but it demonstrates that you understand the paper and, moreover, can summarize it more concisely than the author in his abstract. This paper proposed a new width-based planning and learning agent that can play Atari-2600 games (though it cannot play Montezuma’s Revenge). The author claimed that width-based planning exploration plus (greedy) optimal MDP policy exploitation is able to achieve better performance than Monte-Carlo Tree Search. ...

[TOC] Title: MINDCRAFT: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks Author: Cristian-Paul Bara et. al. Publish Year: 2021 EMNLP Review Date: 12 Nov 2021 Summary of paper This needs to be only 1-3 sentences, but it demonstrates that you understand the paper and, moreover, can summarize it more concisely than the author in his abstract. The contribution of this paper is the mind modelling dataset (Using Minecraft environment). ...