New research revisits action factorization for complex RL spaces · 2 sources tracked

A new research paper explores methods for handling complex action spaces in reinforcement learning, particularly those that combine discrete and continuous actions. The study analyzes various factorization techniques across different algorithms and environments, introducing two new parallel environments, CoopPush and Hybrid-Shoot, to facilitate this research. The findings suggest that branching dueling architectures offer a good balance of compute and performance, with Auto-Regressive actions achieving the highest overall performance, though native continuous SAC proved superior despite higher computational costs. AI

IMPACT This research could lead to more effective reinforcement learning agents capable of handling complex, real-world control tasks.

RANK_REASON The cluster contains a research paper published on arXiv detailing new methods and environments for reinforcement learning.

Read on arXiv cs.LG →

• Atari
• Carla
• cheetah
• CoopPush
• Deep Q-Network
• gymnasium
• LunarLander
• MuJoCo
• PettingZoo: Gym for Multi-Agent Reinforcement Learning
• Proximal Policy Optimization
• RoboCup 2D HFO
• SC2LE
• SeedRL
• sumo
• TorchRL
• Walker2d

• paper
• other

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