6 papers
From Trajectories to Instructions: Language-Conditioned Meta-Reinforcement Learning
Garvit Singla, Uma Maheswari Natarajan, Raghuram Bharadwaj Diddigi
Model-Agnostic Meta-Learning (MAML) is a widely used framework for reinforcement learning (RL) that enables efficient transfer by learning global policy parameters that can be rapi…
Generalisation in Multitask Fitted Q-Iteration and Offline Q-learning
Kausthubh Manda, Raghuram Bharadwaj Diddigi
We study offline multitask reinforcement learning in settings where multiple tasks share a low-rank representation of their action-value functions. In this regime, a learner is pro…
Full-Gradient Successor Feature Representations
Ritish Shrirao, Aditya Priyadarshi, Raghuram Bharadwaj Diddigi
Successor Features (SF) combined with Generalized Policy Improvement (GPI) provide a robust framework for transfer learning in Reinforcement Learning (RL) by decoupling environment…
CLIP-RL: Aligning Language and Policy Representations for Task Transfer in Reinforcement Learning
Chainesh Gautam, Raghuram Bharadwaj Diddigi
Recently, there has been an increasing need to develop agents capable of solving multiple tasks within the same environment, especially when these tasks are naturally associated wi…
Learning Distinguishable Representations in Deep Q-Networks for Linear Transfer
Sooraj Sathish, Keshav Goyal, Raghuram Bharadwaj Diddigi
Deep Reinforcement Learning (RL) has demonstrated success in solving complex sequential decision-making problems by integrating neural networks with the RL framework. However, trai…
Image Generation from Image Captioning -- Invertible Approach
Nandakishore S Menon, Chandramouli Kamanchi, Raghuram Bharadwaj Diddigi
Our work aims to build a model that performs dual tasks of image captioning and image generation while being trained on only one task. The central idea is to train an invertible mo…