The reinforcement learning (RL) research area is very active, with an important number of
new contributions, especially considering the emergent field of deep RL (DRL). However, a …

The reinforcement learning (RL) research area is very active, with an important number of
new contributions; especially considering the emergent field of deep RL (DRL). However a …

Exploration in complex domains is a key challenge in reinforcement learning, especially for
tasks with very sparse rewards. Recent successes in deep reinforcement learning have …

Sparse rewards are double-edged training signals in reinforcement learning: easy to design
but hard to optimize. Intrinsic motivation guidances have thus been developed toward …

Intrinsic rewards can improve exploration in reinforcement learning, but the exploration
process may suffer from instability caused by non-stationary reward shaping and strong …

There is great interest in building intrinsic motivation into artificial systems using the
reinforcement learning framework. Yet, what intrinsic motivation may mean computationally …

Reinforcement learning methods rely on rewards provided by the environment that are
extrinsic to the agent. However, many real-world scenarios involve sparse or delayed …

Learning about many things can provide numerous benefits to a reinforcement learning
system. For example, learning many auxiliary value functions, in addition to optimizing the …

Offline Reinforcement Learning (ORL) enablesus to separately study the two interlinked
processes of reinforcement learning: collecting informative experience and inferring optimal …

Reinforcement Learning enables to train an agent via interaction with the environment.
However, in the majority of real-world scenarios, the extrinsic feedback is sparse or not …