diff --git a/docs/environments.md b/docs/environments.md index 723400d37..71f02a782 100644 --- a/docs/environments.md +++ b/docs/environments.md @@ -149,7 +149,7 @@ Learn more here: https://github.com/Rohan138/gym-legacy-toytext ### gym-spoof -Spoof, otherwise known as "The 3-coin game", is a multi-agent (2 player), imperfect-information, zero-sum game. +Spoof, otherwise known as "The 3-coin game", is a multi-agent (2 player), imperfect-information, zero-sum game. Learn more here: https://github.com/MouseAndKeyboard/gym-spoof @@ -177,7 +177,7 @@ Learn more here: https://github.com/222464/PGE ### gym-inventory: Inventory Control Environments -gym-inventory is a single agent domain featuring discrete state and action spaces that an AI agent might encounter in inventory control problems. +gym-inventory is a single agent domain featuring discrete state and action spaces that an AI agent might encounter in inventory control problems. Learn more here: https://github.com/paulhendricks/gym-inventory @@ -189,7 +189,7 @@ rendering tool. Learn more here: https://github.com/erlerobot/gym-gazebo/ ### gym-maze: 2D maze environment -A simple 2D maze environment where an agent finds its way from the start position to the goal. +A simple 2D maze environment where an agent finds its way from the start position to the goal. Learn more here: https://github.com/tuzzer/gym-maze/ @@ -205,7 +205,7 @@ A minimalistic gridworld environment. Seeks to minimize software dependencies, b Learn more here: https://github.com/maximecb/gym-minigrid -### gym-miniworld: Minimalistic 3D Interior Environment Simulator +### gym-miniworld: Minimalistic 3D Interior Environment Simulator MiniWorld is a minimalistic 3D interior environment simulator for reinforcement learning & robotics research. It can be used to simulate environments with rooms, doors, hallways and various objects (eg: office and home environments, mazes). MiniWorld can be seen as an alternative to VizDoom or DMLab. It is written 100% in Python and designed to be easily modified or extended. @@ -224,7 +224,7 @@ A lane-following simulator built for the [Duckietown](http://duckietown.org/) pr Learn more here: https://github.com/duckietown/gym-duckietown -### GymFC: A flight control tuning and training framework +### GymFC: A flight control tuning and training framework GymFC is a modular framework for synthesizing neuro-flight controllers. The architecture integrates digital twinning concepts to provide seamless transfer @@ -244,7 +244,7 @@ Learn more here: https://github.com/AminHP/gym-anytrading An implementation of the board game Go -Learn more here: https://github.com/aigagror/GymGo +Learn more here: https://github.com/aigagror/GymGo ### gym-electric-motor: Intelligent control of electric drives @@ -276,7 +276,7 @@ gym-carla provides a gym wrapper for the [CARLA simulator](http://carla.org/), w Learn more here: https://github.com/cjy1992/gym-carla -### openmodelica-microgrid-gym: Intelligent control of microgrids +### openmodelica-microgrid-gym: Intelligent control of microgrids The OpenModelica Microgrid Gym (OMG) package is a software toolbox for the simulation and control optimization of microgrids based on energy conversion by power electronic converters. @@ -302,7 +302,7 @@ Learn more here: https://github.com/addy1997/Gridworld ### gym-goddard: Goddard's Rocket Problem -An environment for simulating the classical optimal control problem where the thrust of a vertically ascending rocket shall be determined such that it reaches the maximum possible altitude, while being subject to varying aerodynamic drag, gravity and mass. +An environment for simulating the classical optimal control problem where the thrust of a vertically ascending rocket shall be determined such that it reaches the maximum possible altitude, while being subject to varying aerodynamic drag, gravity and mass. Learn more here: https://github.com/osannolik/gym-goddard @@ -382,13 +382,13 @@ Learn more here: https://github.com/zuoxingdong/gym-recsys ### QASGym: gym environment for Quantum Architecture Search (QAS) -This a list of environments for quantum architecture search following the description in [Quantum Architecture Search via Deep Reinforcement Learning](https://arxiv.org/abs/2104.07715). The agent design the quantum circuit by taking actions in the environment. Each action corresponds to a gate applied on some wires. The goal is to build a circuit U such that generates the target n-qubit quantum state that belongs to the environment and hidden from the agent. The circuits are built using [Google QuantumAI Cirq](https://quantumai.google/cirq). +This a list of environments for quantum architecture search following the description in [Quantum Architecture Search via Deep Reinforcement Learning](https://arxiv.org/abs/2104.07715). The agent design the quantum circuit by taking actions in the environment. Each action corresponds to a gate applied on some wires. The goal is to build a circuit U such that generates the target n-qubit quantum state that belongs to the environment and hidden from the agent. The circuits are built using [Google QuantumAI Cirq](https://quantumai.google/cirq). Learn more here: https://github.com/qdevpsi3/quantum-arch-search ### robo-gym: Environments for Real and Simulated Robots -robo-gym provides a collection of reinforcement learning environments involving robotic tasks applicable in both simulation and real world robotics. +robo-gym provides a collection of reinforcement learning environments involving robotic tasks applicable in both simulation and real world robotics. Learn more here: https://github.com/jr-robotics/robo-gym @@ -428,3 +428,9 @@ Learn more here: https://github.com/dynamik1703/gym_longicontrol PyBullet-based CartPole and Quadrotor environments—with [CasADi](https://web.casadi.org) (symbolic) *a priori* dynamics and constraints—for learning-based control and model-based reinforcement learning. Learn more here: https://github.com/utiasDSL/safe-control-gym + +### MarsExplorer: Deep Reinforcement Learning for Extraterrestrial Exploration + +Mars Explorer is an openai-gym compatible environment designed and developed as an initial endeavor to bridge the gap between powerful Deep Reinforcement Learning methodologies and the problem of exploration/coverage of an unknown terrain. + +Learn more here: https://github.com/dimikout3/MarsExplorer