Search Results for author:
Found 18 papers, 7 papers with code
Generalization Across Observation Shifts in Reinforcement Learning
We focus on bisimulation metrics, which provide a powerful means for abstracting task relevant components of the observation and learning a succinct representation space for training the agent using reinforcement learning.
marl-jax: Multi-Agent Reinforcement Leaning Framework
We present marl-jax, a multi-agent reinforcement learning software package for training and evaluating social generalization of the agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Trust-Region-Free Policy Optimization for Stochastic Policies
In this paper, we show that the trust region constraint over policies can be safely substituted by a trust-region-free constraint without compromising the underlying monotonic improvement guarantee.
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning
In this work, we conduct new analysis demonstrating that SMAC lacks the stochasticity and partial observability to require complex *closed-loop* policies.
Effects of Spectral Normalization in Multi-agent Reinforcement Learning
A reliable critic is central to on-policy actor-critic learning.
Generalization in Cooperative Multi-Agent Systems
Specifically, we study generalization bounds under a linear dependence of the underlying dynamics on the agent capabilities, which can be seen as a generalization of Successor Features to MAS.
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions
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, factored action spaces using tensor decompositions.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Model based Multi-agent Reinforcement Learning with Tensor Decompositions
A challenge in multi-agent reinforcement learning is to be able to generalize over intractable state-action spaces.
Model-based Reinforcement Learning
Multi-agent Reinforcement Learning
+3
Open-Ended Learning Leads to Generally Capable Agents
The resulting space is exceptionally diverse in terms of the challenges posed to agents, and as such, even measuring the learning progress of an agent is an open research problem.
SoftDICE for Imitation Learning: Rethinking Off-policy Distribution Matching
We present SoftDICE, which achieves state-of-the-art performance for imitation learning.
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning
Algorithms derived from Tesseract decompose the Q-tensor across agents and utilise low-rank tensor approximations to model agent interactions relevant to the task.
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning
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.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
RODE: Learning Roles to Decompose Multi-Agent Tasks
Learning a role selector based on action effects makes role discovery much easier because it forms a bi-level learning hierarchy -- the role selector searches in a smaller role space and at a lower temporal resolution, while role policies learn in significantly reduced primitive action-observation spaces.
MAVEN: Multi-Agent Variational Exploration
We specifically focus on QMIX [40], the current state-of-the-art in this domain.
VIREL: A Variational Inference Framework for Reinforcement Learning
This gives VIREL a mode-seeking form of KL divergence, the ability to learn deterministic optimal polices naturally from inference and the ability to optimise value functions and policies in separate, iterative steps.
Symmetry Learning for Function Approximation in Reinforcement Learning
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 recent advances in the use of deep networks for complex RL tasks which require large amount of training data.
Lifted Inference Rules With Constraints
Lifted inference rules exploit symmetries for fast reasoning in statistical rela-tional models.
