Add MarsExplorer (https://github.com/dimikout3/MarsExplorer), a third party environment (#2389)

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