• 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...
  • 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...
  • Grad-CAM

  • Referenced in 4 articles [sw35098]
  • tasks with multimodal inputs or reinforcement learning, without any architectural changes or re-training...
  • Flappy

  • Referenced in 2 articles [sw38067]
  • learning control algorithms such as Reinforcement Learning. The interface of the simulation is fully compatible ... hovering stabilization and a Deep Reinforcement Learning control policy for goal-directed maneuvering. Finally...
  • 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...
  • 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...
  • MazeBase

  • Referenced in 3 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...
  • RLDDE

  • Referenced in 3 articles [sw35866]
  • RLDDE: A novel reinforcement learning-based dimension and delay estimator for neural networks in time ... time delay. A novel method, called reinforcement learning-based dimension and delay estimator (RLDDE...
  • mdp

  • Referenced in 3 articles [sw21994]
  • Norvig. It does not implement reinforcement learning or POMDPs. For a very similar package...
  • 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...
  • Tianshou

  • Referenced in 1 article [sw35091]
  • Tianshou is a reinforcement learning platform based on pure PyTorch. Unlike existing reinforcement learning libraries ... pythonic API for building the deep reinforcement learning agent with the least number of lines...
  • 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...
  • rlpyt

  • Referenced in 1 article [sw31152]
  • Research Code Base for Deep Reinforcement Learning in PyTorch. Since the recent advent of deep ... reinforcement learning for game play and simulated robotic control, a multitude of new algorithms have ... categorized into three families: deep Q-learning, policy gradients, and Q-value policy gradients. These ... great depth of common deep reinforcement learning machinery. We are pleased to share rlpyt, which...
  • OpenGraphGym

  • Referenced in 1 article [sw38102]
  • OpenGraphGym: A Parallel Reinforcement Learning Framework for Graph Optimization Problems. This paper presents an open ... OpenGraphGym) to facilitate the application of reinforcement learning (RL) algorithms to address combinatorial graph optimization ... This environment incorporates a basic deep reinforcement learning method, and several graph embeddings to capture...
  • 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...
  • keras-rl

  • Referenced in 2 articles [sw35087]
  • some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with...
  • Stable Baselines

  • Referenced in 2 articles [sw34408]
  • improved implementations of reinforcement learning algorithms based on OpenAI Baselines...
  • ProMP

  • Referenced in 2 articles [sw34914]
  • Policy Search. Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood. Existing...