  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 policy optimization obtaining human-like behavior. Results
confirm the utility of DIAMBRA Arena as a reinforcement learning research tool,
providing environments designed to study some of the most challenging topics in
the field.
