2 papers
cs.LG2026
Simplicial Embeddings Improve Sample Efficiency in Actor-Critic Agents
Johan Obando-Ceron, Walter Mayor, Samuel Lavoie +3
Recent works have proposed accelerating the wall-clock training time of actor-critic methods via the use of large-scale environment parallelization; unfortunately, these can someti…
cs.LG2025
The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks
Walter Mayor, Johan Obando-Ceron, Aaron Courville +1
The use of parallel actors for data collection has been an effective technique used in reinforcement learning (RL) algorithms. The manner in which data is collected in these algori…