… Building upon previous attempts to merge reinforcement learning and planning, … planning
algorithms. Similar to [5] [24] [9], we learn goal-conditioned policies via reinforcement learning …

… We present a Reinforcement Learning (RL) solution to the view planning problem (VPP),
which generates a sequence of view points that are capable of sensing all accessible area of a …

… As a result, this paper uses time-series and machine learning models within a reinforcement
learning approach to leverage available data and fast responsiveness to new conditions. …

… local planning involves optimizing a policy with respect to a future discounted reward. The
primary goal of deep reinforcement learning within our framework is therefore to learn optimal …

… of research in reinforcement learning and decision-theoretic planning either assumes only
a … familiar with single-objective reinforcement learning and planning methods who wish to …

… This paper introduces a general framework for tactical decision making, which combines …
planning and learning, in the form of Monte Carlo tree search and deep reinforcement learning. …

… Planning The final part of the end-end pipeline is the Reinforcement learning planning part.
This network follows the same training procedure of the DQN, with a Q-network on the top …

… The pseudocode of reinforcement learning with additional planning is detailed in Algorithm
3. According to the two different approaches introduced in Section 3.1, the two RLAP …

… learning algorithms. To do so, we define sufficient criteria for a sample-based planner to be
used in such a learning … We also introduce our own sample-based planner, which combines …

… In this section, we show that DDPG and SAC can learn optimal trajectory planning for
dynamic obstacles collision avoidance. For the evaluation, we compare two different DRL …