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Found 8 papers, 1 papers with code
Value Explicit Pretraining for Learning Transferable Representations
We propose Value Explicit Pretraining (VEP), a method that learns generalizable representations for transfer reinforcement learning.
Evaluating Pretrained models for Deployable Lifelong Learning
We create a novel benchmark for evaluating a Deployable Lifelong Learning system for Visual Reinforcement Learning (RL) that is pretrained on a curated dataset, and propose a novel Scalable Lifelong Learning system capable of retaining knowledge from the previously learnt RL tasks.
Bird's Eye View Based Pretrained World model for Visual Navigation
We trained the model using data from a Differential drive robot in the CARLA simulator.
Lightweight Learner for Shared Knowledge Lifelong Learning
We propose a new Shared Knowledge Lifelong Learning (SKILL) challenge, which deploys a decentralized population of LL agents that each sequentially learn different tasks, with all agents operating independently and in parallel.
What can we learn from misclassified ImageNet images?
Hence, we propose a two-stage Super-Sub framework, and demonstrate that: (i) The framework improves overall classification performance by 3. 3%, by first inferring a superclass using a generalist superclass-level network, and then using a specialized network for final subclass-level classification.
Shaped Policy Search for Evolutionary Strategies using Waypoints
In this paper, we try to improve exploration in Blackbox methods, particularly Evolution strategies (ES), when applied to Reinforcement Learning (RL) problems where intermediate waypoints/subgoals are available.
Attentive Feature Reuse for Multi Task Meta learning
Our method improves performance on new, previously unseen environments, and is 1. 5x faster than standard existing meta learning methods using similar architectures.
Meta Adaptation using Importance Weighted Demonstrations
Imitation learning has gained immense popularity because of its high sample-efficiency.
