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The rise and potential of large language model based agents: A survey
For a long time, researchers have sought artificial intelligence (AI) that matches or exceeds
human intelligence. AI agents, which are artificial entities capable of sensing the …
human intelligence. AI agents, which are artificial entities capable of sensing the …
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. We …
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
Scaling laws for reward model overoptimization
In reinforcement learning from human feedback, it is common to optimize against a reward
model trained to predict human preferences. Because the reward model is an imperfect …
model trained to predict human preferences. Because the reward model is an imperfect …
A survey of zero-shot generalisation in deep reinforcement learning
The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to
produce RL algorithms whose policies generalise well to novel unseen situations at …
produce RL algorithms whose policies generalise well to novel unseen situations at …
Leveraging procedural generation to benchmark reinforcement learning
Abstract We introduce Procgen Benchmark, a suite of 16 procedurally generated game-like
environments designed to benchmark both sample efficiency and generalization in …
environments designed to benchmark both sample efficiency and generalization in …
Quantifying generalization in reinforcement learning
In this paper, we investigate the problem of overfitting in deep reinforcement learning.
Among the most common benchmarks in RL, it is customary to use the same environments …
Among the most common benchmarks in RL, it is customary to use the same environments …
Loss of plasticity in continual deep reinforcement learning
In this paper, we characterize the behavior of canonical value-based deep reinforcement
learning (RL) approaches under varying degrees of non-stationarity. In particular, we …
learning (RL) approaches under varying degrees of non-stationarity. In particular, we …
Stabilizing deep q-learning with convnets and vision transformers under data augmentation
While agents trained by Reinforcement Learning (RL) can solve increasingly challenging
tasks directly from visual observations, generalizing learned skills to novel environments …
tasks directly from visual observations, generalizing learned skills to novel environments …
Deep reinforcement learning
Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …
decision strategies. However, in many cases, it is desirable to learn directly from …
Stop regressing: Training value functions via classification for scalable deep rl
Value functions are a central component of deep reinforcement learning (RL). These
functions, parameterized by neural networks, are trained using a mean squared error …
functions, parameterized by neural networks, are trained using a mean squared error …