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08/17/2023
Diversifying AI: Towards Creative Chess with AlphaZero
In recent years, Artificial Intelligence (AI) systems have surpassed hum...
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03/07/2023
Structured State Space Models for In-Context Reinforcement Learning
Structured state space sequence (S4) models have recently achieved state...
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02/28/2023
Hierarchical Reinforcement Learning in Complex 3D Environments
Hierarchical Reinforcement Learning (HRL) agents have the potential to d...
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01/28/2023
Composing Task Knowledge with Modular Successor Feature Approximators
Recently, the Successor Features and Generalized Policy Improvement (SF ...
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12/30/2022
POMRL: No-Regret Learning-to-Plan with Increasing Horizons
We study the problem of planning under model uncertainty in an online me...
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10/30/2022
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction
The Bayes-Adaptive Markov Decision Process (BAMDP) formalism pursues the...
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10/25/2022
In-context Reinforcement Learning with Algorithm Distillation
We propose Algorithm Distillation (AD), a method for distilling reinforc...
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10/19/2022
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining
Large and diverse datasets have been the cornerstones of many impressive...
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06/30/2022
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
We introduce DeepNash, an autonomous agent capable of learning to play t...
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02/08/2022
GrASP: Gradient-Based Affordance Selection for Planning
Planning with a learned model is arguably a key component of intelligenc...
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11/01/2021
On the Expressivity of Markov Reward
Reward is the driving force for reinforcement-learning agents. This pape...
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09/09/2021
Bootstrapped Meta-Learning
Meta-learning empowers artificial intelligence to increase its efficienc...
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06/01/2021
Reward is enough for convex MDPs
Maximising a cumulative reward function that is Markov and stationary, i...
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02/25/2021
Reinforcement Learning of Implicit and Explicit Control Flow in Instructions
Learning to flexibly follow task instructions in dynamic environments po...
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02/12/2021
Discovery of Options via Meta-Learned Subgoals
Temporal abstractions in the form of options have been shown to help rei...
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02/09/2021
Pairwise Weights for Temporal Credit Assignment
How much credit (or blame) should an action taken in a state get for a f...
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02/09/2021
Learning State Representations from Random Deep Action-conditional Predictions
In this work, we study auxiliary prediction tasks defined by temporal-di...
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12/14/2020
Efficient Querying for Cooperative Probabilistic Commitments
Multiagent systems can use commitments as the core of a general coordina...
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10/28/2020
Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in First-person Simulated 3D Environments
First-person object-interaction tasks in high-fidelity, 3D, simulated en...
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07/17/2020
Discovering Reinforcement Learning Algorithms
Reinforcement learning (RL) algorithms update an agent's parameters acco...
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07/16/2020
Meta-Gradient Reinforcement Learning with an Objective Discovered Online
Deep reinforcement learning includes a broad family of algorithms that p...
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06/08/2020
Learning to Play No-Press Diplomacy with Best Response Policy Iteration
Recent advances in deep reinforcement learning (RL) have led to consider...
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02/28/2020
Self-Tuning Deep Reinforcement Learning
Reinforcement learning (RL) algorithms often require expensive manual or...
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12/15/2019
How Should an Agent Practice?
We present a method for learning intrinsic reward functions to drive the...
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12/11/2019
What Can Learned Intrinsic Rewards Capture?
Reinforcement learning agents can include different components, such as ...
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12/05/2019
Hindsight Credit Assignment
We consider the problem of efficient credit assignment in reinforcement ...
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11/25/2019
Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem
Order dispatching and driver repositioning (also known as fleet manageme...
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10/31/2019
Object-oriented state editing for HRL
We introduce agents that use object-oriented reasoning to consider alter...
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10/23/2019
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles
Reinforcement learning (RL) methods have been shown to be capable of lea...
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09/10/2019
Discovery of Useful Questions as Auxiliary Tasks
Arguably, intelligent agents ought to be able to discover their own ques...
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09/04/2019
No Press Diplomacy: Modeling Multi-Agent Gameplay
Diplomacy is a seven-player non-stochastic, non-cooperative game, where ...
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08/09/2019
Behaviour Suite for Reinforcement Learning
This paper introduces the Behaviour Suite for Reinforcement Learning, or...
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01/24/2019
Learning Independently-Obtainable Reward Functions
We present a novel method for learning a set of disentangled reward func...
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12/03/2018
Generative Adversarial Self-Imitation Learning
This paper explores a simple regularizer for reinforcement learning by p...
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08/24/2018
Learning End-to-End Goal-Oriented Dialog with Multiple Answers
In a dialog, there can be multiple valid next utterances at any point. T...
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06/22/2018
Many-Goals Reinforcement Learning
All-goals updating exploits the off-policy nature of Q-learning to updat...
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06/14/2018
Self-Imitation Learning
This paper proposes Self-Imitation Learning (SIL), a simple off-policy a...
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04/22/2018
Named Entities troubling your Neural Methods? Build NE-Table: A neural approach for handling Named Entities
Many natural language processing tasks require dealing with Named Entiti...
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04/17/2018
On Learning Intrinsic Rewards for Policy Gradient Methods
In many sequential decision making tasks, it is challenging to design re...
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03/08/2018
The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA
During the 2017 NBA playoffs, Celtics coach Brad Stevens was faced with ...
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11/15/2017
Markov Decision Processes with Continuous Side Information
We consider a reinforcement learning (RL) setting in which the agent int...
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07/11/2017
Value Prediction Network
This paper proposes a novel deep reinforcement learning (RL) architectur...
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06/15/2017
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning
As a step towards developing zero-shot task generalization capabilities ...
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05/15/2017
Repeated Inverse Reinforcement Learning
We introduce a novel repeated Inverse Reinforcement Learning problem: th...
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05/30/2016
Control of Memory, Active Perception, and Action in Minecraft
In this paper, we introduce a new set of reinforcement learning (RL) tas...
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01/25/2016
Towards Resolving Unidentifiability in Inverse Reinforcement Learning
We consider a setting for Inverse Reinforcement Learning (IRL) where the...
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07/31/2015
Action-Conditional Video Prediction using Deep Networks in Atari Games
Motivated by vision-based reinforcement learning (RL) problems, in parti...
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01/16/2014
Learning to Make Predictions In Partially Observable Environments Without a Generative Model
When faced with the problem of learning a model of a high-dimensional en...
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01/23/2013
Approximate Planning for Factored POMDPs using Belief State Simplification
We are interested in the problem of planning for factored POMDPs. Buildi...
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01/23/2013
