Reinforcement Learning
PettingZoo: A Standard API for Multi-Agent Reinforcement Learning J. K. Terry
This paper introduces the PettingZoo library and the accompanying Agent Environment Cycle ("AEC") games model. PettingZoo is a library of diverse sets of multi-agent environments with a universal, elegant Python API. PettingZoo was developed with the goal of accelerating research in Multi-Agent Reinforcement Learning ("MARL "), by making work more interchangeable, accessible and reproducible akin to what OpenAI's Gym library did for single-agent reinforcement
DecisionTransformer: Reinforcement LearningviaSequenceModeling
This stands insharp contrast tomuch workinreinforcement learning (RL), which learns a single policy to model a particular narrow behavior distribution. Given the diversity of applications andimpact oftransformer models, weseek toexamine their application tosequential decision making problems.
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method
Based on our regularization, we propose an off-policy actor-critic algorithm.
Inherently Explainable Reinforcement Learning in Natural Language
Observation: Up a tree Beside you on the branch is a small birds nest In the birds nest is a large egg encrusted with precious jewels, scavenged by a childless songbird... Explanation: I am in the Forest Path now.
