This paper reviews exploration techniques in deep reinforcement learning. Exploration
techniques are of primary importance when solving sparse reward problems. In sparse …

Since its introduction, the reward prediction error theory of dopamine has explained a wealth
of empirical phenomena, providing a unifying framework for understanding the …

Offline Reinforcement Learning (RL) aims to learn policies from previously collected
datasets without exploring the environment. Directly applying off-policy algorithms to offline …

Reinforcement learning agents have demonstrated remarkable achievements in simulated
environments. Data efficiency poses an impediment to carrying this success over to real …

Deep reinforcement learning (DRL) and deep multiagent reinforcement learning (MARL)
have achieved significant success across a wide range of domains, including game artificial …

The first comprehensive guide to distributional reinforcement learning, providing a new
mathematical formalism for thinking about decisions from a probabilistic perspective …

Reinforcement learning agents are faced with two types of uncertainty. Epistemic uncertainty
stems from limited data and is useful for exploration, whereas aleatoric uncertainty arises …

Generative flow networks (GFlowNets) are amortized variational inference algorithms that
treat sampling from a distribution over compositional objects as a sequential decision …

The roundabout is a typical changeable, interactive scenario in which automated vehicles
should make adaptive and safe decisions. In this article, an optimization embedded …

In fully cooperative multi-agent reinforcement learning (MARL) settings, the environments
are highly stochastic due to the partial observability of each agent and the continuously …