Reinforcement Learning (RL) has emerged as an effective approach to address a variety of
complex control tasks. In a typical RL problem, an agent interacts with the environment by …

In recent decades, Reinforcement Learning (RL) has emerged as an effective approach to
address complex control tasks. In a Markov Decision Process (MDP), the framework typically …

In many real-world problems, there is the possibility to configure, to a limited extent, some
environmental parameters to improve the performance of a learning agent. In this paper, we …

Abstract Configurable Markov Decision Processes (Conf-MDPs) have been recently
introduced as an extension of the usual MDP model to account for the possibility of …

Reinforcement Learning (RL) algorithms have had tremendous success in simulated
domains. These algorithms, however, often cannot be directly applied to physical systems …

Abstract The Configurable Markov Decision Process framework includes two entities: a
Reinforcement Learning agent and a configurator that can modify some environmental …

One of the central challenges faced by a reinforcement learning (RL) agent is to effectively
learn a (near-) optimal policy in environments with large state spaces having sparse and …

In Reinforcement Learning (RL) the goal is to find a policy π that maximizes the expected
future return, calculated based on a scalar reward function R (·)∈ R. The policy π …

We study the problem of identifying the policy space available to an agent in a learning
process, having access to a set of demonstrations generated by the agent playing the …

Producing agents that can generalize to a wide range of environments is a significant
challenge in reinforcement learning. One method for overcoming this issue is domain …