17 citations · 30 across the 12 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
Neural Network Compatible Off-Policy Natural Actor-Critic Algorithm
Raghuram Bharadwaj Diddigi, Prateek Jain, Prabuchandran K. J. +1
Learning optimal behavior from existing data is one of the most important problems in Reinforcement Learning (RL). This is known as "off-policy control" in RL where an agent's obje…
A Convergent Off-Policy Temporal Difference Algorithm
Raghuram Bharadwaj Diddigi, Chandramouli Kamanchi, Shalabh Bhatnagar
Learning the value function of a given policy (target policy) from the data samples obtained from a different policy (behavior policy) is an important problem in Reinforcement Lear…