… Therefore, this paper introduces the Framework for Reinforcement learning and Planning
(FRAP), which identifies the essential algorithmic decisions that any planning or RL algorithm …

… framework for reinforcement learning and planning (FRAP), which identifies underlying
dimensions on which MDP planning and learning … variety of well-known planning, model-free and …

… On the other hand, end-to-end learning methods cannot assure the safety of the … planning
framework with a set of low-level safe controllers and a high-level reinforcement learning …

… up reinforcement learning. This paper compares eight reinforcement learning frameworks:
adaptive heuristic critic (AHC) learning due to Sutton, Q-learning due to Watkins, and three …

… of reinforcement learning (RL) techniques in the production planning and control (PPC) field
addressing the following PPC areas: facility resource planning, capacity planning, purchase …

… [26] used a multi-agent Reinforcement Learning framework to … framework, which combines
Reinforcement Learning and Simulation, that can be exploited to tackle long-term planning …

… We evaluate both online and offline learning reinforcement learning paradigms for the
case of maintenance planning. We report the average intervention cost and failure probability …

… , a general motion planning framework that integrates deep reinforcement learning (DRL) is
… First, based on the maximum entropy framework and Tsallis entropy, a DRL algorithm with …

… Local path planning in unknown dynamic … local path planner n-step dueling double DQN
with reward-based ϵ -greedy (RND3QN) based on a deep reinforcement learning framework, …

… Recent works present deep reinforcement learning as a framework to model the complex
interactions and cooperation. However, they are implemented using key assumptions about …