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Found 16 papers, 11 papers with code
Disentangling the Causes of Plasticity Loss in Neural Networks
Underpinning the past decades of work on the design, initialization, and optimization of neural networks is a seemingly innocuous assumption: that the network is trained on a \textit{stationary} data distribution.
Toward Human-AI Alignment in Large-Scale Multi-Player Games
First, we analyze extensive human gameplay data from Xbox's Bleeding Edge (100K+ games), uncovering behavioral patterns in a complex task space.
Forecaster: Towards Temporally Abstract Tree-Search Planning from Pixels
The ability to plan at many different levels of abstraction enables agents to envision the long-term repercussions of their decisions and thus enables sample-efficient learning.
Discovering Object-Centric Generalized Value Functions From Pixels
Deep Reinforcement Learning has shown significant progress in extracting useful representations from high-dimensional inputs albeit using hand-crafted auxiliary tasks and pseudo rewards.
POMRL: No-Regret Learning-to-Plan with Increasing Horizons
We study the problem of planning under model uncertainty in an online meta-reinforcement learning (RL) setting where an agent is presented with a sequence of related tasks with limited interactions per task.
The Paradox of Choice: Using Attention in Hierarchical Reinforcement Learning
Decision-making AI agents are often faced with two important challenges: the depth of the planning horizon, and the branching factor due to having many choices.
Temporally Abstract Partial Models
Humans and animals have the ability to reason and make predictions about different courses of action at many time scales.
Sequoia: A Software Framework to Unify Continual Learning Research
We propose a taxonomy of settings, where each setting is described as a set of assumptions.
Variance Penalized On-Policy and Off-Policy Actor-Critic
Reinforcement learning algorithms are typically geared towards optimizing the expected return of an agent.
Towards Continual Reinforcement Learning: A Review and Perspectives
In this article, we aim to provide a literature review of different formulations and approaches to continual reinforcement learning (RL), also known as lifelong or non-stationary RL.
Learning Robust State Abstractions for Hidden-Parameter Block MDPs
Further, we provide transfer and generalization bounds based on task and state similarity, along with sample complexity bounds that depend on the aggregate number of samples across tasks, rather than the number of tasks, a significant improvement over prior work that use the same environment assumptions.
What can I do here? A Theory of Affordances in Reinforcement Learning
Gibson (1977) coined the term "affordances" to describe the fact that certain states enable an agent to do certain actions, in the context of embodied agents.
Options of Interest: Temporal Abstraction with Interest Functions
Temporal abstraction refers to the ability of an agent to use behaviours of controllers which act for a limited, variable amount of time.
Environments for Lifelong Reinforcement Learning
To achieve general artificial intelligence, reinforcement learning (RL) agents should learn not only to optimize returns for one specific task but also to constantly build more complex skills and scaffold their knowledge about the world, without forgetting what has already been learned.
Attend Before you Act: Leveraging human visual attention for continual learning
When humans perform a task, such as playing a game, they selectively pay attention to certain parts of the visual input, gathering relevant information and sequentially combining it to build a representation from the sensory data.
Safe Option-Critic: Learning Safety in the Option-Critic Architecture
We propose an optimization objective that learns safe options by encouraging the agent to visit states with higher behavioural consistency.
