Most policy search (PS) algorithms require thousands of training episodes to find an
effective policy, which is often infeasible with a physical robot. This survey article focuses on …

We introduce a new algorithm for reinforcement learning called Maximum aposteriori Policy
Optimisation (MPO) based on coordinate ascent on a relative entropy objective. We show …

A widely-used actor-critic reinforcement learning algorithm for continuous control, Deep
Deterministic Policy Gradients (DDPG), suffers from the overestimation problem, which can …

Optimization of parameterized policies for reinforcement learning (RL) is an important and
challenging problem in artificial intelligence. Among the most common approaches are …

Active Inference is a theory arising from theoretical neuroscience which casts action and
planning as Bayesian inference problems to be solved by minimizing a single quantity—the …

Scaling decision theoretic planning to large multiagent systems is challenging due to
uncertainty and partial observability in the environment. We focus on a multiagent planning …

Deep multi-agent reinforcement learning (MARL) holds the promise of automating many real-
world cooperative robotic manipulation and transportation tasks. Nevertheless …

How can we effectively exploit the collected samples when solving a continuous control task
with Reinforcement Learning? Recent results have empirically demonstrated that multiple …

Proximal policy optimization (PPO) is a deep reinforcement learning algorithm based on the
actor–critic (AC) architecture. In the classic AC architecture, the Critic (value) network is used …

We reformulate the option framework as two parallel augmented MDPs. Under this novel
formulation, all policy optimization algorithms can be used off the shelf to learn intra-option …