DIAMBRA Arena: A New Reinforcement Learning Platform For Research And Experimentation

Abstract

The recent advances in reinforcement learning have led to effective methods able to obtain above human-level performances in very complex environments. However, once solved, these environments become less valuable, and new challenges with different or more complex scenarios are needed to support research advances. This work presents DIAMBRA Arena, a new platform for reinforcement learning research and experimentation, featuring a collection of high-quality environments exposing a Python API fully compliant with OpenAI Gym standard. They are episodic tasks with discrete actions and observations composed by raw pixels plus additional numerical values, all supporting both single player and two players mode, allowing to work on standard reinforcement learning, competitive multi-agent, human-agent competition, self-play, human-in-the-loop training and imitation learning. Software capabilities are demonstrated by successfully training multiple deep reinforcement learning agents with proximal

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keypalmas2022diambra

Related papers

• The AI Arena: A Framework For Distributed Multi-agent Reinforcement Learning (2021)0.00
• Google Research Football: A Novel Reinforcement Learning Environment (2019)17.22
• Is Deep Reinforcement Learning Really Superhuman On Atari? Leveling The Playing Field (2019)0.00
• Deep Multiagent Reinforcement Learning: Challenges And Directions (2021)0.00
• A Human Mixed Strategy Approach To Deep Reinforcement Learning (2018)7.50
• Jumanji: A Diverse Suite Of Scalable Reinforcement Learning Environments In JAX (2023)0.00
• Multi-agent Deep Reinforcement Learning With Human Strategies (2018)8.09
• Simplified Action Decoder For Deep Multi-agent Reinforcement Learning (2019)4.03