Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Continual Reinforcement Learning with TELLA (2208.04287v1)

Published 8 Aug 2022 in cs.LG

Abstract: Training reinforcement learning agents that continually learn across multiple environments is a challenging problem. This is made more difficult by a lack of reproducible experiments and standard metrics for comparing different continual learning approaches. To address this, we present TELLA, a tool for the Test and Evaluation of Lifelong Learning Agents. TELLA provides specified, reproducible curricula to lifelong learning agents while logging detailed data for evaluation and standardized analysis. Researchers can define and share their own curricula over various learning environments or run against a curriculum created under the DARPA Lifelong Learning Machines (L2M) Program.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (5)
  1. Neil Fendley (9 papers)
  2. Cash Costello (3 papers)
  3. Eric Nguyen (11 papers)
  4. Gino Perrotta (2 papers)
  5. Corey Lowman (5 papers)
Citations (2)

Summary

We haven't generated a summary for this paper yet.