Search Results for author:
Found 12 papers, 5 papers with code
A Survey of Meta-Reinforcement Learning
Meta-RL is most commonly studied in a problem setting where, given a distribution of tasks, the goal is to learn a policy that is capable of adapting to any new task from the task distribution with as little data as possible.
Generalized Beliefs for Cooperative AI
Self-play is a common paradigm for constructing solutions in Markov games that can yield optimal policies in collaborative settings.
On the Practical Consistency of Meta-Reinforcement Learning Algorithms
We further find that theoretically inconsistent algorithms can be made consistent by continuing to update all agent components on the OOD tasks, and adapt as well or better than originally consistent ones.
Communicating via Markov Decision Processes
We contribute a theoretically grounded approach to MCGs based on maximum entropy reinforcement learning and minimum entropy coupling that we call MEME.
Optimizing piano practice with a utility-based scaffold
Ideally, a focus on a particular practice method should be made in a way to maximize the learner's progress in learning to play the piano.
A Self-Supervised Auxiliary Loss for Deep RL in Partially Observable Settings
In this work we explore an auxiliary loss useful for reinforcement learning in environments where strong performing agents are required to be able to navigate a spatial environment.
ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition
To close this gap, we present the ORBIT dataset and benchmark, grounded in the real-world application of teachable object recognizers for people who are blind/low-vision.
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning
The optimal adaptive behaviour under uncertainty over the other agents' strategies w. r. t.
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
To rapidly learn a new task, it is often essential for agents to explore efficiently -- especially when performance matters from the first timestep.
VIABLE: Fast Adaptation via Backpropagating Learned Loss
In few-shot learning, typically, the loss function which is applied at test time is the one we are ultimately interested in minimising, such as the mean-squared-error loss for a regression problem.
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning.
Deep Variational Reinforcement Learning for POMDPs
Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown.
