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Walter Mayor

2 accepted papers

2026

Simplicial Embeddings Improve Sample Efficiency in Actor–Critic Agents

ICLR 2026poster

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 sometimes still require large number of environment interactions to achieve a desired level of performance. Noting that well-s…

Cited by 0SourceScholar
2025

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks

ICML 2025poster

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 algorithms, controlled via the number of parallel environments and the rollout length, induces a form of bias-variance trade-o…

Cited by 0SourcePDFScholar