59 citations · 221 across the 105 of their papers we have counts for
14 papers · 1 filter
Zero Shot Coordination for Sparse Reward Tasks with Diverse Reward Shapings
Keenan Powell, Peihong Yu, Pratap Tokekar
Many Multi-Agent Reinforcement Learning (MARL) agents fail to adapt properly to cooperating with agents trained with the same objectives but different seeds, algorithms, or other t…
Active Asymmetric Multi-Agent Multimodal Learning under Uncertainty
Rui Liu, Pratap Tokekar, Ming Lin
Multi-agent systems are increasingly equipped with heterogeneous multimodal sensors, enabling richer perception but introducing modality-specific and agent-dependent uncertainty. E…
Stable and Efficient Single-Rollout RL for Multimodal Reasoning
Rui Liu, Dian Yu, Lei Ke +6
Reinforcement Learning with Verifiable Rewards (RLVR) has become a key paradigm to improve the reasoning capabilities of Multimodal Large Language Models (MLLMs). However, prevalen…
Option Discovery Using LLM-guided Semantic Hierarchical Reinforcement Learning
Chak Lam Shek, Pratap Tokekar
Large Language Models (LLMs) have shown remarkable promise in reasoning and decision-making, yet their integration with Reinforcement Learning (RL) for complex robotic tasks remain…
PEnGUiN: Partially Equivariant Graph NeUral Networks for Sample Efficient MARL
Joshua McClellan, Greyson Brothers, Furong Huang +1
Equivariant Graph Neural Networks (EGNNs) have emerged as a promising approach in Multi-Agent Reinforcement Learning (MARL), leveraging symmetry guarantees to greatly improve sampl…
Adaptive Conformal Guidance for Learning under Uncertainty
Rui Liu, Peng Gao, Yu Shen +2
Learning with guidance has proven effective across a wide range of machine learning systems. Guidance may, for example, come from annotated datasets in supervised learning, pseudo-…
Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance
Joshua McClellan, Naveed Haghani, John Winder +2
Multi-Agent Reinforcement Learning (MARL) struggles with sample inefficiency and poor generalization [1]. These challenges are partially due to a lack of structure or inductive bia…
On the Global Optimality of Policy Gradient Methods in General Utility Reinforcement Learning
Anas Barakat, Souradip Chakraborty, Peihong Yu +2
Reinforcement learning with general utilities (RLGU) offers a unifying framework to capture several problems beyond standard expected returns, including imitation learning, pure ex…
Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis
Rui Liu, Anish Gupta, Erfaun Noorani +1
Reinforcement Learning (RL) has shown exceptional performance across various applications, enabling autonomous agents to learn optimal policies through interaction with their envir…
Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information
Vishnu Dutt Sharma, John P. Dickerson, Pratap Tokekar
Green Security Games with real-time information (GSG-I) add the real-time information about the agents' movement to the typical GSG formulation. Prior works on GSG-I have used deep…
Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies
Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel +2
In this paper, we present a novel Heavy-Tailed Stochastic Policy Gradient (HT-PSG) algorithm to deal with the challenges of sparse rewards in continuous control problems. Sparse re…
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning
Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel +4
Model-based approaches to reinforcement learning (MBRL) exhibit favorable performance in practice, but their theoretical guarantees in large spaces are mostly restricted to the set…
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces
Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil +3
We focus on parameterized policy search for reinforcement learning over continuous action spaces. Typically, one assumes the score function associated with a policy is bounded, whi…
Risk-Aware Planning by Confidence Estimation using Deep Learning-Based Perception
Maymoonah Toubeh, Pratap Tokekar
This work proposes the use of Bayesian approximations of uncertainty from deep learning in a robot planner, showing that this produces more cautious actions in safety-critical scen…