C-Procgen: Empowering Procgen with Controllable Contexts

• URL: http://arxiv.org/abs/2311.07312v1
• Date: Mon, 13 Nov 2023 13:07:48 GMT
• Title: C-Procgen: Empowering Procgen with Controllable Contexts
• Authors: Zhenxiong Tan, Kaixin Wang and Xinchao Wang
• Abstract summary: C-Procgen is an enhanced suite of environments on top of the Procgen benchmark. It provides access to over 200 unique game contexts across 16 games.
• Score: 62.84544720338002
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present C-Procgen, an enhanced suite of environments on top of the Procgen benchmark. C-Procgen provides access to over 200 unique game contexts across 16 games. It allows for detailed configuration of environments, ranging from game mechanics to agent attributes. This makes the procedural generation process, previously a black-box in Procgen, more transparent and adaptable for various research needs.The upgrade enhances dynamic context management and individualized assignments, while maintaining computational efficiency. C-Procgen's controllable contexts make it applicable in diverse reinforcement learning research areas, such as learning dynamics analysis, curriculum learning, and transfer learning. We believe that C-Procgen will fill a gap in the current literature and offer a valuable toolkit for future works.

Related papers

• clembench-2024: A Challenging, Dynamic, Complementary, Multilingual Benchmark and Underlying Flexible Framework for LLMs as Multi-Action Agents [19.989503513817095]
  Large Language Models can be prompted to "self-play" conversational games that probe certain capabilities. We take one of the proposed frameworks for setting up such game-play environments, and test its usefulness as an evaluation instrument.
  arXiv  Detail & Related papers  (2024-05-31T14:43:31Z)
• RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios [58.62407014256686]
  RealGen is a novel retrieval-based in-context learning framework for traffic scenario generation. RealGen synthesizes new scenarios by combining behaviors from multiple retrieved examples in a gradient-free way. This in-context learning framework endows versatile generative capabilities, including the ability to edit scenarios.
  arXiv  Detail & Related papers  (2023-12-19T23:11:06Z)
• CUDC: A Curiosity-Driven Unsupervised Data Collection Method with Adaptive Temporal Distances for Offline Reinforcement Learning [62.58375643251612]
  We propose a Curiosity-driven Unsupervised Data Collection (CUDC) method to expand feature space using adaptive temporal distances for task-agnostic data collection. With this adaptive reachability mechanism in place, the feature representation can be diversified, and the agent can navigate itself to collect higher-quality data with curiosity. Empirically, CUDC surpasses existing unsupervised methods in efficiency and learning performance in various downstream offline RL tasks of the DeepMind control suite.
  arXiv  Detail & Related papers  (2023-12-19T14:26:23Z)
• CITB: A Benchmark for Continual Instruction Tuning [44.40322919392584]
  Continual learning (CL) is a paradigm that aims to replicate the human ability to learn and accumulate knowledge continually. Recent instruction tuning (IT) involves fine-tuning models to make them more adaptable to solving NLP tasks in general. We establish a benchmark consisting of learning and evaluation protocols to study various CL methods systematically.
  arXiv  Detail & Related papers  (2023-10-23T02:42:32Z)
• Cross-Episodic Curriculum for Transformer Agents [26.240903251696874]
  We present a new algorithm, Cross-Episodic Curriculum ( CEC), to boost the learning efficiency and generalization of Transformer agents. Central to CEC is the placement of cross-episodic experiences into a Transformer's context. CEC constructs curricula that encapsulate learning progression and proficiency increase across episodes.
  arXiv  Detail & Related papers  (2023-10-12T17:45:05Z)
• SimCS: Simulation for Domain Incremental Online Continual Segmentation [60.18777113752866]
  Existing continual learning approaches mostly focus on image classification in the class-incremental setup. We propose SimCS, a parameter-free method complementary to existing ones that uses simulated data to regularize continual learning.
  arXiv  Detail & Related papers  (2022-11-29T14:17:33Z)
• Realistic simulation of users for IT systems in cyber ranges [63.20765930558542]
  We instrument each machine by means of an external agent to generate user activity. This agent combines both deterministic and deep learning based methods to adapt to different environment. We also propose conditional text generation models to facilitate the creation of conversations and documents.
  arXiv  Detail & Related papers  (2021-11-23T10:53:29Z)
• Learning Multi-Objective Curricula for Deep Reinforcement Learning [55.27879754113767]
  Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL) In this paper, we propose a unified automatic curriculum learning framework to create multi-objective but coherent curricula. In addition to existing hand-designed curricula paradigms, we further design a flexible memory mechanism to learn an abstract curriculum.
  arXiv  Detail & Related papers  (2021-10-06T19:30:25Z)
• Meta Automatic Curriculum Learning [35.13646854355393]
  We introduce the concept of Meta-ACL, and formalize it in the context of black-box RL learners. We present AGAIN, a first instantiation of Meta-ACL, and showcase its benefits for curriculum generation over classical ACL.
  arXiv  Detail & Related papers  (2020-11-16T14:56:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.