Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and BaselinesKeerthiram MurugesanMattia AtzeniPavan KapanipathiPushkar ShuklaSadhana KumaravelGerald TesauroKartik TalamadupulaMrinmaya SachanMurray CampbellASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCENational Conference on Artificial ...
Using Transformers to create RL agents suited for real-world tasks Bottom line: “Transformers for RL!” —Senior Research Software Engineer Ricky Loynd Publication Working Memory Graphs Quick glance: “Working Memory Graphs” presents a new reinforcement lear...
We expose OpenAI's gym environments and extends the malware_rl repo to allow researchers to develop Reinforcement Learning agents to bypass three malware detectors, including Ember (Endgame Malware BEnchmark for Research) (paper), MalConv, and FireEye. During the policy learning phase, the agent ...
Key: propose a competitive framework for LLM-based agents; build a simulated competitive environment ExpEnv: a virtual town with only restaurants and customers Model-based Reinforcement Learning for Parameterized Action Spaces Renhao Zhang, Haotian Fu, Yilin Miao, George Konidaris Key: discrete-continu...
templates and vocabulary words that are recognized by the game, normally hidden from players in a non-human-readable format, for agents to choose from. The template example “take ___ from ___” could result in a successful action when combined ...
Generalizing vision-based reinforcement learning (RL) agents to novel environments remains a difficult and open challenge. Current trends are to collect large-scale datasets or use data augmentation techniques to prevent overfitting and improve downstream generalization. However, the computational and data ...
A mechanism for achieving coordination in multi-agent RL through rewarding agents for having causal Influence over other agents actions. Actions that lead to bigger changes in other agents behavior are considered influential and are rewarded.
[RL-based、Other agent、continual learning] Online Continual Learning for Interactive Instruction Following Agents [RL-based、LLM as tool] Leveraging Large Language Models for Optimised Coordination in Textual Multi-Agent Reinforcement Learning [RL-based、LLM as tool] Text2Reward: Dense Reward Generation...
Human intuitive theory 包括: Object-oriented world exists an abstract and causal mechanism of physical objects, intentional agents, and their interactions uses human-like intuitive theories to explore and model an environment, and plan effectively to achieve task goals. ...
A text-based game generator and extensible sandbox learning environment for training and testing reinforcement learning (RL) agents. Also check outaka.ms/textworldfor more info about TextWorld and its creators. Have questions or feedback about TextWorld? Send them totextworld@microsoft.comor use the...