Exploration is a key problem in reinforcement learning, since agents can only learn from
data they acquire in the environment. With that in mind, maintaining a population of agents is …

Applying reinforcement learning (RL) to combinatorial optimization problems is attractive as
it removes the need for expert knowledge or pre-solved instances. However, it is unrealistic …

We introduce SCAL, an algorithm designed to perform efficient exploration-exploration in
any unknown weakly-communicating Markov Decision Process (MDP) for which an upper …

Efficient exploration remains a challenging research problem in reinforcement learning,
especially when an environment contains large state spaces, deceptive local optima, or …

Reinforcement Learning (RL) has achieved impressive performance in many complex
environments due to the integration with Deep Neural Networks (DNNs). At the same time …

Exploration is an essential component of reinforcement learning algorithms, where agents
need to learn how to predict and control unknown and often stochastic environments …

Reinforcement learning is about learning agent models that make the best sequential
decisions in unknown environments. In an unknown environment, the agent needs to …

Efficient exploration is one of the main challenges in reinforcement learning (RL). Most
existing sample-efficient algorithms assume the existence of a single reward function during …

Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep
neural networks roughly as well as Q-learning and policy gradient methods on challenging …

Both genetic algorithms (GAs) and temporal difference (TD) methods have proven effective
at solving reinforcement learning (RL) problems. However, since few rigorous empirical …