Social reasoning necessitates the capacity of theory of mind (ToM), the ability to
contextualise and attribute mental states to others without having access to their internal …

Existing Deep Reinforcement Learning (DRL) algorithms suffer from sample inefficiency.
Generally, episodic control-based approaches are solutions that leverage highly-rewarded …

The sparsity of reward feedback remains a challenging problem in online deep
reinforcement learning (DRL). Previous approaches have utilized temporal credit …

One of the long-term goals of reinforcement learning is to build intelligent agents capable of
rapidly learning and flexibly transferring skills, similar to humans and animals. In this paper …

Non-parametric episodic memory can be used to quickly latch onto high-rewarded
experience in reinforcement learning tasks. In contrast to parametric deep reinforcement …

We introduce a novel training procedure for policy gradient methods wherein episodic
memory is used to optimize the hyperparameters of reinforcement learning algorithms on …

We introduce a constrained optimization method for policy gradient reinforcement learning,
which uses two trust regions to regulate each policy update. In addition to using the …

What motivates agents to explore? Successfully answering this question would enable
agents to learn efficiently in formidable tasks. Random explorations such as 𝜖-greedy are …

Prompt optimization is essential for enhancing the performance of Large Language Models
(LLMs) in a range of Natural Language Processing (NLP) tasks, particularly in scenarios of …

Episodic memory is often illustrated with the madeleine de Proust excerpt as the ability to re-
experience a situation from the past following the perception of a stimulus. This simplistic …