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Found 7 papers, 1 papers with code
Human-Timescale Adaptation in an Open-Ended Task Space
Foundation models have shown impressive adaptation and scalability in supervised and self-supervised learning problems, but so far these successes have not fully translated to reinforcement learning (RL).
Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality
Finding different solutions to the same problem is a key aspect of intelligence associated with creativity and adaptation to novel situations.
Entropic Desired Dynamics for Intrinsic Control
An agent might be said, informally, to have mastery of its environment when it has maximised the effective number of states it can reliably reach.
Self-Consistent Models and Values
Learned models of the environment provide reinforcement learning (RL) agents with flexible ways of making predictions about the environment.
Learning more skills through optimistic exploration
However, an inherent exploration problem lingers: when a novel state is actually encountered, the discriminator will necessarily not have seen enough training data to produce accurate and confident skill classifications, leading to low intrinsic reward for the agent and effective penalization of the sort of exploration needed to actually maximize the objective.
Relative Variational Intrinsic Control
In the absence of external rewards, agents can still learn useful behaviors by identifying and mastering a set of diverse skills within their environment.
Acme: A Research Framework for Distributed Reinforcement Learning
These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research.
