… producing exploration strategies that … learning exploration strategies when compared to
prior meta-RL methods, RL without learned exploration strategies, and task-agnostic exploration …

… baseline agents employing the contemporary exploration approaches are easily trapped
by … our exploration strategy are able to overcome aforementioned challenges, and learn …

… learning approach taken by the algorithm and the domain the agent is acting in. In this work,
our goal is to learn the best exploration strategies … predefined exploration strategies across a …

… , the next actions a t + 1 are sampled from the behaviour policy which follows an ε -greedy
exploration strategy, and among them, the action that makes the largest Q-value, a ′ , is …

… robots learning to walk around a deceptive trap. This paper thus introduces a family of fast,
scalable algorithms for reinforcement learning that are capable of directed exploration. It also …

… A fundamental issue in reinforcement learning algorithms is the balance between exploration
… This paper surveys exploration strategies used in reinforcement learning and summarizes …

… exploration algorithms are known to perform near-optimally when used in conjunction with
tabular reinforcement learning (… Recent deep RL exploration strategies are able to deal with …

… collected by the E/E strategy over an infinite length trajectory. We … a rich set of candidate E/E
strategies and by looking for the one that … E/E strategies, we consider index-based strategies …

… robust and effective robotic exploration strategies, suitable to … integration of robotics with
reinforcement learning (RL) tech… design unknown environment exploration strategies for single …

… Abstract—This paper introduces a reinforcement learning method for exploring a corridor
environment with the depth information from an RGB-D sensor only. The robot controller …