75 citations · 284 across the 16 of their papers we have counts for
17 papers
Agent Plasticity: Measuring Self-Improvement Through Experience
Harman Singh, Anton Bakhtin, Rulin Shao +8
AI agents increasingly operate in environments where they can diagnose failures and improve through experience, yet existing evaluations largely measure what an agent can do at a f…
Generalization Across Observation Shifts in Reinforcement Learning
Anuj Mahajan, Amy Zhang
Learning policies which are robust to changes in the environment are critical for real world deployment of Reinforcement Learning agents. They are also necessary for achieving good…
marl-jax: Multi-Agent Reinforcement Leaning Framework
Kinal Mehta, Anuj Mahajan, Pawan Kumar
Recent advances in Reinforcement Learning (RL) have led to many exciting applications. These advancements have been driven by improvements in both algorithms and engineering, which…
Trust-Region-Free Policy Optimization for Stochastic Policies
Mingfei Sun, Benjamin Ellis, Anuj Mahajan +3
Trust Region Policy Optimization (TRPO) is an iterative method that simultaneously maximizes a surrogate objective and enforces a trust region constraint over consecutive policies…
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning
Benjamin Ellis, Jonathan Cook, Skander Moalla +5
The availability of challenging benchmarks has played a key role in the recent progress of machine learning. In cooperative multi-agent reinforcement learning, the StarCraft Multi-…
Effects of Spectral Normalization in Multi-agent Reinforcement Learning
Kinal Mehta, Anuj Mahajan, Pawan Kumar
A reliable critic is central to on-policy actor-critic learning. But it becomes challenging to learn a reliable critic in a multi-agent sparse reward scenario due to two factors: 1…
Generalization in Cooperative Multi-Agent Systems
Anuj Mahajan, Mikayel Samvelyan, Tarun Gupta +4
Collective intelligence is a fundamental trait shared by several species of living organisms. It has allowed them to thrive in the diverse environmental conditions that exist on ou…
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions
Anuj Mahajan, Mikayel Samvelyan, Lei Mao +6
We present an extended abstract for the previously published work TESSERACT [Mahajan et al., 2021], which proposes a novel solution for Reinforcement Learning (RL) in large, factor…
Model based Multi-agent Reinforcement Learning with Tensor Decompositions
Pascal Van Der Vaart, Anuj Mahajan, Shimon Whiteson
A challenge in multi-agent reinforcement learning is to be able to generalize over intractable state-action spaces. Inspired from Tesseract [Mahajan et al., 2021], this position pa…
Open-Ended Learning Leads to Generally Capable Agents
Open Ended Learning Team, Adam Stooke, Anuj Mahajan +15
In this work we create agents that can perform well beyond a single, individual task, that exhibit much wider generalisation of behaviour to a massive, rich space of challenges. We…
SoftDICE for Imitation Learning: Rethinking Off-policy Distribution Matching
Mingfei Sun, Anuj Mahajan, Katja Hofmann +1
We present SoftDICE, which achieves state-of-the-art performance for imitation learning. SoftDICE fixes several key problems in ValueDICE, an off-policy distribution matching appro…
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning
Anuj Mahajan, Mikayel Samvelyan, Lei Mao +6
Reinforcement Learning in large action spaces is a challenging problem. Cooperative multi-agent reinforcement learning (MARL) exacerbates matters by imposing various constraints on…
RODE: Learning Roles to Decompose Multi-Agent Tasks
Tonghan Wang, Tarun Gupta, Anuj Mahajan +3
Role-based learning holds the promise of achieving scalable multi-agent learning by decomposing complex tasks using roles. However, it is largely unclear how to efficiently discove…
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning
Tarun Gupta, Anuj Mahajan, Bei Peng +2
VDN and QMIX are two popular value-based algorithms for cooperative MARL that learn a centralized action value function as a monotonic mixing of per-agent utilities. While this ena…
MAVEN: Multi-Agent Variational Exploration
Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan +1
Centralised training with decentralised execution is an important setting for cooperative deep multi-agent reinforcement learning due to communication constraints during execution…
VIREL: A Variational Inference Framework for Reinforcement Learning
Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner +1
Applying probabilistic models to reinforcement learning (RL) enables the application of powerful optimisation tools such as variational inference to RL. However, existing inference…
Symmetry Learning for Function Approximation in Reinforcement Learning
Anuj Mahajan, Theja Tulabandhula
In this paper we explore methods to exploit symmetries for ensuring sample efficiency in reinforcement learning (RL), this problem deserves ever increasing attention with the recen…