Given a simple request like Put a washed apple in the kitchen fridge, humans can reason in
purely abstract terms by imagining action sequences and scoring their likelihood of success …

Effective exploration is a challenge in reinforcement learning (RL). Novelty-based
exploration methods can suffer in high-dimensional state spaces, such as continuous …

Reinforcement learning (RL) agents are particularly hard to train when rewards are sparse.
One common solution is to use intrinsic rewards to encourage agents to explore their …

We present a representation-driven framework for reinforcement learning. By representing
policies as estimates of their expected values, we leverage techniques from contextual …

Learning chess strategies has been investigated widely, with most studies focussing on
learning from previous games using search algorithms. Chess textbooks encapsulate …

Recent advances in deep reinforcement learning have showcased its potential in tackling
complex tasks. However, experiments on visual control tasks have revealed that state-of-the …

World Models are made of generative networks that can predict future states of a single
environment which it was trained on. This research proposes a Multi-world Model, a …

This paper investigates the potential of using natural language descriptions as an alternative
to direct image-based observations for learning policies in reinforcement learning. Due to …

Reinforcement learning has received significant interest in recent years, due primarily to the
successes of deep reinforcement learning at solving many challenging tasks such as …

Recent advances in large language models (LLMs) have inspired research on their potential
for robots in real-world tasks. This study investigates whether the architecture of the vision …