• Jason

  • Referenced in 61 articles [sw06187]
  • systems using the Jason platform; it reinforces learning with examples, problems, and illustrations, includes...
  • Approxrl

  • Referenced in 28 articles [sw14312]
  • implementations of a number of approximate reinforcement learning (RL) and dynamic programming (DP) algorithms. Notably ... Schutter, and D. Ernst, Reinforcement Learning and Dynamic Programming Using Function Approximators, CRC Press, Automation...
  • R-MAX

  • Referenced in 32 articles [sw02539]
  • very simple model-based reinforcement learning algorithm which can attain near-optimal average reward...
  • OpenAI Gym

  • Referenced in 15 articles [sw15330]
  • OpenAI Gym is a toolkit for reinforcement learning research. It includes a growing collection...
  • TEXPLORE

  • Referenced in 7 articles [sw13721]
  • TEXPLORE: temporal difference reinforcement learning for robots and time-constrained domains This book presents ... develops new reinforcement learning methods that enable fast and robust learning on robots in real ... that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential...
  • RL-Glue

  • Referenced in 8 articles [sw13720]
  • Glue: Language-Independent Software for Reinforcement-Learning Experiments. RL-Glue is a standard, language-independent ... software package for reinforcement-learning experiments. The standardization provided by RL-Glue facilitates code sharing...
  • PyBrain

  • Referenced in 10 articles [sw12670]
  • still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test ... PyBrain is short for Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library...
  • SeqGAN

  • Referenced in 7 articles [sw26534]
  • generator as a stochastic policy in reinforcement learning (RL), SeqGAN bypasses the generator differentiation problem...
  • Dopamine

  • Referenced in 4 articles [sw31151]
  • Dopamine: A Research Framework for Deep Reinforcement Learning. Deep reinforcement learning (deep RL) research...
  • RLlib

  • Referenced in 4 articles [sw31153]
  • RLlib: Scalable Reinforcement Learning. RLlib is an open-source library for reinforcement learning that offers...
  • PEORL

  • Referenced in 3 articles [sw29437]
  • PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making. Reinforcement learning ... used to build intelligent autonomous agents. Reinforcement learning relies on learning from interactions with real ... that integrates symbolic planning with hierarchical reinforcement learning (HRL) to cope with decision-making...
  • ELF

  • Referenced in 4 articles [sw26533]
  • Lightweight and Flexible platform for fundamental reinforcement learning research. Using ELF, we implement a highly ... notebook. When coupled with modern reinforcement learning methods, the system can train a full-game...
  • RLgraph

  • Referenced in 2 articles [sw31155]
  • RLgraph: Modular Computation Graphs for Deep Reinforcement Learning. Reinforcement learning (RL) tasks are challenging ... library for designing and executing reinforcement learning tasks in both static graph and define...
  • Tensorforce

  • Referenced in 2 articles [sw31158]
  • Tensorforce: a TensorFlow library for applied reinforcement learning. Tensorforce is an open-source deep reinforcement ... learning framework, with an emphasis on modularized flexible library design and straightforward usability for applications ... Full-on TensorFlow models: The entire reinforcement learning logic, including control flow, is implemented...
  • Horizon

  • Referenced in 2 articles [sw31157]
  • Horizon: Facebook’s Open Source Applied Reinforcement Learning Platform. In this paper we present Horizon ... Facebook’s open source applied reinforcement learning (RL) platform. Horizon ... showcase and describe real examples where reinforcement learning models trained with Horizon significantly outperformed...
  • rllib

  • Referenced in 2 articles [sw11544]
  • template-based reinforcement learning library: fitting the code to the mathematics. This paper introduces ... good fit between the mathematics of reinforcement learning and their implementation in a library...
  • SBEED

  • Referenced in 2 articles [sw34727]
  • SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation. When function approximation is used, solving ... remained a major open problem in reinforcement learning for decades. The fundamental difficulty is that...
  • RALF

  • Referenced in 2 articles [sw33955]
  • RALF - Reinforced Active Learning Formulation. RALF is the framework used in [1] and part ... This framework combines active learning and reinforcement learning to enable a time-varying trade...
  • L-VIBRA

  • Referenced in 3 articles [sw02429]
  • last years the use of on-line learning approaches to achieve coordination has attracted ... this work is to use a Reinforcement Learning approach in the job of learning...
  • Ray

  • Referenced in 3 articles [sw28740]
  • will continuously interact with the environment and learn from these interactions. These applications impose ... existing specialized systems for several challenging reinforcement learning applications...