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Found 12 papers, 4 papers with code
Vision-Language Models as a Source of Rewards
Building generalist agents that can accomplish many goals in rich open-ended environments is one of the research frontiers for reinforcement learning.
Structured State Space Models for In-Context Reinforcement Learning
We propose a modification to a variant of S4 that enables us to initialise and reset the hidden state in parallel, allowing us to tackle reinforcement learning tasks.
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).
Meta-Gradients in Non-Stationary Environments
We support these results with a qualitative analysis of resulting meta-parameter schedules and learned functions of context features.
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.
Bootstrapped Meta-Learning
We achieve a new state-of-the art for model-free agents on the Atari ALE benchmark and demonstrate that it yields both performance and efficiency gains in multi-task meta-learning.
Universal Value Density Estimation for Imitation Learning and Goal-Conditioned Reinforcement Learning
This work considers two distinct settings: imitation learning and goal-conditioned reinforcement learning.
Active Learning within Constrained Environments through Imitation of an Expert Questioner
Active learning agents typically employ a query selection algorithm which solely considers the agent's learning objectives.
Generative predecessor models for sample-efficient imitation learning
We propose Generative Predecessor Models for Imitation Learning (GPRIL), a novel imitation learning algorithm that matches the state-action distribution to the distribution observed in expert demonstrations, using generative models to reason probabilistically about alternative histories of demonstrated states.
Imitating Latent Policies from Observation
In this paper, we describe a novel approach to imitation learning that infers latent policies directly from state observations.
State Aware Imitation Learning
Imitation learning is the study of learning how to act given a set of demonstrations provided by a human expert.
State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning
To address this issue, we develop a framework through which a deep RL agent learns to generalize policies from smaller, simpler domains to more complex ones using a recurrent attention mechanism.
