3 accepted papers
We develop a novel approach to determine the optimal policy in entropy-regularized reinforcement learning (RL) with stochastic dynamics. For deterministic dynamics, the optimal policy can be derived using Bayesian inference in the control-as-inference framework; however, for stochastic dynamics, the…
In the field of reinforcement learning (RL), agents are often tasked with solving a variety of problems differing only in their reward functions. In order to quickly obtain solutions to unseen problems with new reward functions, a popular approach involves functional composition of previously solved…
In reinforcement learning (RL), the ability to utilize prior knowledge from previously solved tasks can allow agents to quickly solve new problems. In some cases, these new problems may be approximately solved by composing the solutions of previously solved primitive tasks (task composition). Otherw…