Search Results for author:
Found 7 papers, 3 papers with code
Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments
Visualizing optimization landscapes has led to many fundamental insights in numeric optimization, and novel improvements to optimization techniques.
A revised lower estimate of ozone columns during Earth's oxygenated history
Using a three-dimensional chemistry-climate model, we simulate changes in O$_3$ in Earth's atmosphere since the GOE and consider the implications for surface habitability, and glaciation during the Mesoproterozoic.
Earth and Planetary Astrophysics Atmospheric and Oceanic Physics Geophysics
PettingZoo: Gym for Multi-Agent Reinforcement Learning
This paper introduces the PettingZoo library and the accompanying Agent Environment Cycle ("AEC") games model.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Agent Environment Cycle Games
Partially Observable Stochastic Games (POSGs) are the most general and common model of games used in Multi-Agent Reinforcement Learning (MARL).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Multiplayer Support for the Arcade Learning Environment
The Arcade Learning Environment ("ALE") is a widely used library in the reinforcement learning community that allows easy programmatic interfacing with Atari 2600 games, via the Stella emulator.
SuperSuit: Simple Microwrappers for Reinforcement Learning Environments
In reinforcement learning, wrappers are universally used to transform the information that passes between a model and an environment.
Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement Learning
Next, we formally introduce methods to extend parameter sharing to learning in heterogeneous observation and action spaces, and prove that these methods allow for convergence to optimal policies.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
