• Jason

  • Referenced in 56 articles [sw06187]
  • systems using the Jason platform; it reinforces learning with examples, problems, and illustrations, includes...
  • Approxrl

  • Referenced in 25 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...
  • 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 9 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...
  • OpenAI Gym

  • Referenced in 6 articles [sw15330]
  • OpenAI Gym is a toolkit for reinforcement learning research. It includes a growing collection...
  • ELF

  • Referenced in 3 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...
  • PEORL

  • Referenced in 2 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...
  • RLPy

  • Referenced in 2 articles [sw14300]
  • RLPy: a value-function-based reinforcement learning framework for education and research. RLPy ... object-oriented reinforcement learning software package with a focus on value-function-based methods using ... functions), facilitating recently increased specialization in reinforcement learning. RLPy is written in Python to allow ... properties allow users to compare various reinforcement learning algorithms with little effort...
  • 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...
  • 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...
  • APES

  • Referenced in 1 article [sw26484]
  • APES: a Python toolbox for simulating reinforcement learning environments. Assisted by neural networks, reinforcement learning ... essential component in any reinforcement learning problem. The environment simulates the dynamics of the agents ... create 2D grid-world environments for reinforcement learning problems. APES equips agents with algorithms...
  • Metacar

  • Referenced in 1 article [sw27153]
  • Metacar: A reinforcement learning environment for self-driving cars in the browser. Metacar ... reinforcement learning environment for autonomous vehicles running in the browser. The project aims ... reinforcement learning be more accessible to everyone through solving fun problems. Metacar comes with...
  • bandit-nmt

  • Referenced in 1 article [sw20941]
  • bandit-nmt: Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback. Machine translation ... natural candidate problem for reinforcement learning from human feedback: users provide quick, dirty ratings ... generated reference translations. We describe a reinforcement learning algorithm that improves neural machine translation systems...
  • Piqle

  • Referenced in 2 articles [sw14313]
  • fast design, prototyping and test of reinforcement learning experiments (RL). By clearly separating algorithms...
  • MazeBase

  • Referenced in 2 articles [sw26504]
  • games, designed as a sandbox for machine learning approaches to reasoning and planning. Within ... network, memory network) are deployed via reinforcement learning on these games, with and without...
  • mdp

  • Referenced in 2 articles [sw21994]
  • Norvig. It does not implement reinforcement learning or POMDPs. For a very similar package...
  • Ray

  • Referenced in 2 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...
  • BindsNET

  • Referenced in 1 article [sw30217]
  • specifically geared towards machine learning and reinforcement learning. Our software, called BindsNET, enables rapid building ... evaluation of spiking networks on reinforcement learning problems. We argue that this package facilitates...