Reinforcement Learning Environment

Reinforcement learning (RL) environments are simulated worlds designed to train RL agents, aiming to create realistic and challenging scenarios for efficient learning and robust policy development. Current research emphasizes improving data efficiency through techniques like curriculum learning and action masking, developing environments with complex, multi-objective structures (often incorporating safety mechanisms), and creating standardized, modular, and extensible frameworks for environment creation. These advancements are crucial for advancing RL research and enabling its application in diverse fields, from cybersecurity and robotics to supply chain optimization and design verification.

Papers

September 7, 2022

Hearts Gym: Learning Reinforcement Learning as a Team Event
Jan Ebert, Danimir T. Doncevic, Ramona Kloß, Stefan Kesselheim
Reinforcement Learning Reward Shaping Reinforcement Learning Environment MR iNet Gym Team Sport Learning Hand on Electronics

July 7, 2022

gym-DSSAT: a crop model turned into a Reinforcement Learning environment
Romain Gautron, Emilio J. Padrón, Philippe Preux, Julien Bigot, Odalric-Ambrym Maillard, David Emukpere
Reinforcement Learning Reinforcement Learning Environment Maize Plant Crop Simulation Crop Model

July 5, 2022

Planning with RL and episodic-memory behavioral priors
Shivansh Beohar, Andrew Melnik
Imitation Learning Task Planning Deep Q Learning Human Like RL Reinforcement Learning Environment Based Exploration

June 21, 2022

May 31, 2022

IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents
Artem Zholus, Alexey Skrynnik, Shrestha Mohanty, Zoya Volovikova, Julia Kiseleva, Artur Szlam, Marc-Alexandre Coté, Aleksandr I. Panov
Agent Smith Interactive Learning Reinforcement Learning Environment Grid World Visual Agent Embodied Conversational Agent

May 30, 2022

A Simulation Environment and Reinforcement Learning Method for Waste Reduction
Sami Jullien, Mozhdeh Ariannezhad, Paul Groth, Maarten de Rijke
Reinforcement Learning Distributional Reinforcement Learning Reinforcement Learning Environment Reinforcement Learning Task Advanced Textile Recycling

May 17, 2022

DeepSim: A Reinforcement Learning Environment Build Toolkit for ROS and Gazebo
Woong Gyu La, Lingjie Kong, Sunil Muralidhara, Pratik Nichat
Robotics Domain Ro Package Reinforcement Learning Environment Gazebo Simulation DeepSim Network

May 14, 2022

Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments
Ryan Sullivan, J. K. Terry, Benjamin Black, John P. Dickerson
Reinforcement Learning Reinforcement Learning Environment Optimization Landscape Cliff Function

March 2, 2022

Reliable validation of Reinforcement Learning Benchmarks
Matthias Müller-Brockhausen, Aske Plaat, Mike Preuss
Reinforcement Learning Offline Reinforcement Learning Experimental Validation Reinforcement Learning Benchmark Game AI Reinforcement Learning Environment Interpretable Reward

February 24, 2022

Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share?
Alain Andres, Esther Villar-Rodriguez, Javier Del Ser
Environment Feature Sparse Reward Intrinsic Reward Heterogeneous Agent Intrinsic Motivation Reinforcement Learning Environment Collaborative Training Exploration Strategy Heterogeneous Multi Agent Reinforcement Learning

January 19, 2022

Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams
Dongjie Wang, Kunpeng Liu, Hui Xiong, Yanjie Fu
Link Stream Temporal Interaction Reinforcement Learning Environment Interactive Reinforcement Learning POI Recommendation

December 6, 2021

Hierarchical Reinforcement Learning with Timed Subgoals
Nico Gürtler, Dieter Büchler, Georg Martius
Hierarchical Reinforcement Learning Reinforcement Learning Environment Subgoal Search