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08/24/2023
Bayesian Exploration Networks
Bayesian reinforcement learning (RL) offers a principled and elegant app...
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05/19/2023
The Waymo Open Sim Agents Challenge
Simulation with realistic, interactive agents represents a key task for ...
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03/19/2023
Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning
By enabling agents to communicate, recent cooperative multi-agent reinfo...
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02/24/2023
Why Target Networks Stabilise Temporal Difference Methods
Integral to recent successes in deep reinforcement learning has been a c...
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02/22/2023
Universal Morphology Control via Contextual Modulation
Learning a universal policy across different robot morphologies can sign...
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02/15/2023
Trust-Region-Free Policy Optimization for Stochastic Policies
Trust Region Policy Optimization (TRPO) is an iterative method that simu...
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01/19/2023
A Survey of Meta-Reinforcement Learning
While deep reinforcement learning (RL) has fueled multiple high-profile ...
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12/21/2022
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios
Imitation learning (IL) is a simple and powerful way to use high-quality...
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12/14/2022
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning
The availability of challenging benchmarks has played a key role in the ...
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12/14/2022
Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving
Multi-object state estimation is a fundamental problem for robotic appli...
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12/02/2022
Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula
ML-based motion planning is a promising approach to produce agents that ...
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10/21/2022
Equivariant Networks for Zero-Shot Coordination
Successful coordination in Dec-POMDPs requires agents to adopt robust st...
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10/20/2022
Hypernetworks in Meta-Reinforcement Learning
Training a reinforcement learning (RL) agent on a real-world robotics ta...
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10/18/2022
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving
We demonstrate the first large-scale application of model-based generati...
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09/22/2022
An Investigation of the Bias-Variance Tradeoff in Meta-Gradients
Meta-gradients provide a general approach for optimizing the meta-parame...
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06/26/2022
Generalized Beliefs for Cooperative AI
Self-play is a common paradigm for constructing solutions in Markov game...
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05/06/2022
Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation
Simulation is a crucial tool for accelerating the development of autonom...
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01/31/2022
Monotonic Improvement Guarantees under Non-stationarity for Decentralized PPO
We present a new monotonic improvement guarantee for optimizing decentra...
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01/31/2022
You May Not Need Ratio Clipping in PPO
Proximal Policy Optimization (PPO) methods learn a policy by iteratively...
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01/11/2022
In Defense of the Unitary Scalarization for Deep Multi-Task Learning
Recent multi-task learning research argues against unitary scalarization...
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12/11/2021
Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency
Sample efficiency is crucial for imitation learning methods to be applic...
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12/01/2021
On the Practical Consistency of Meta-Reinforcement Learning Algorithms
Consistency is the theoretical property of a meta learning algorithm tha...
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10/27/2021
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions
We present an extended abstract for the previously published work TESSER...
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10/27/2021
Model based Multi-agent Reinforcement Learning with Tensor Decompositions
A challenge in multi-agent reinforcement learning is to be able to gener...
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08/11/2021
Truncated Emphatic Temporal Difference Methods for Prediction and Control
Emphatic Temporal Difference (TD) methods are a class of off-policy Rein...
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07/17/2021
Implicit Communication as Minimum Entropy Coupling
In many common-payoff games, achieving good performance requires players...
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06/09/2021
Bayesian Bellman Operators
We introduce a novel perspective on Bayesian reinforcement learning (RL)...
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06/06/2021
SoftDICE for Imitation Learning: Rethinking Off-policy Distribution Matching
We present SoftDICE, which achieves state-of-the-art performance for imi...
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05/31/2021
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning
Reinforcement Learning in large action spaces is a challenging problem. ...
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03/22/2021
Regularized Softmax Deep Multi-Agent Q-Learning
Tackling overestimation in Q-learning is an important problem that has b...
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03/01/2021
Snowflake: Scaling GNNs to High-Dimensional Continuous Control via Parameter Freezing
Recent research has shown that Graph Neural Networks (GNNs) can learn po...
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01/21/2021
Breaking the Deadly Triad with a Target Network
The deadly triad refers to the instability of a reinforcement learning a...
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01/11/2021
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning
Agents that interact with other agents often do not know a priori what t...
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01/08/2021
Average-Reward Off-Policy Policy Evaluation with Function Approximation
We consider off-policy policy evaluation with function approximation (FA...
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10/06/2020
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning
This paper focuses on cooperative value-based multi-agent reinforcement ...
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10/05/2020
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control
Multitask Reinforcement Learning is a promising way to obtain models wit...
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10/02/2020
A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms
We investigate the discounting mismatch in actor-critic algorithm implem...
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10/02/2020
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
Meta-learning is a powerful tool for learning policies that can adapt ef...
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09/21/2020
Exploiting Submodular Value Functions For Scaling Up Active Perception
In active perception tasks, an agent aims to select sensory actions that...
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07/17/2020
WordCraft: An Environment for Benchmarking Commonsense Agents
The ability to quickly solve a wide range of real-world tasks requires a...
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07/09/2020
Learning Retrospective Knowledge with Reverse Reinforcement Learning
We present a Reverse Reinforcement Learning (Reverse RL) approach for re...
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06/18/2020
Weighted QMIX: Expanding Monotonic Value Function Factorisation
QMIX is a popular Q-learning algorithm for cooperative MARL in the centr...
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06/10/2020
The Impact of Non-stationarity on Generalisation in Deep Reinforcement Learning
Non-stationarity arises in Reinforcement Learning (RL) even in stationar...
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06/07/2020
AI-QMIX: Attention and Imagination for Dynamic Multi-Agent Reinforcement Learning
Real world multi-agent tasks often involve varying types and quantities ...
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05/19/2020
Privileged Information Dropout in Reinforcement Learning
Using privileged information during training can improve the sample effi...
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05/11/2020
Maximizing Information Gain in Partially Observable Environments via Prediction Reward
Information gathering in a partially observable environment can be formu...
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04/22/2020
Per-Step Reward: A New Perspective for Risk-Averse Reinforcement Learning
We present a new per-step reward perspective for risk-averse control in ...
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03/19/2020
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
In many real-world settings, a team of agents must coordinate its behavi...
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03/14/2020
Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control
Deep multi-agent reinforcement learning (MARL) holds the promise of auto...
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02/26/2020
