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Career

· Co-founder And CTO, Anyscale2019–
· PhD Student, UC Berkeley2013–2019

Publications (22)

Trust Region Policy Optimization
International Conference on Machine Learning · 2015
7,583
cited
High-Dimensional Continuous Control Using Generalized Advantage Estimation
International Conference on Learning Representations · 2015
4,104
cited
Ray: A Distributed Framework for Emerging AI Applications
USENIX Symposium on Operating Systems Design and Implementation · 2017
1,516
cited
Tune: A Research Platform for Distributed Model Selection and Training
arXiv.org · 2018
1,056
cited
RLlib: Abstractions for Distributed Reinforcement Learning
International Conference on Machine Learning · 2017
981
cited
A Linearly-Convergent Stochastic L-BFGS Algorithm
International Conference on Artificial Intelligence and Statistics · 2015
253
cited
Ray RLLib: A Composable and Scalable Reinforcement Learning Library
Neural Information Processing Systems · 2017
178
cited
SparkNet: Training Deep Networks in Spark
International Conference on Learning Representations · 2015
175
cited
Real-Time Machine Learning: The Missing Pieces
USENIX Workshop on Hot Topics in Operating Systems · 2017
64
cited
Lineage stash: fault tolerance off the critical path
Symposium on Operating Systems Principles · 2019
55
cited
Policy Gradient Search: Online Planning and Expert Iteration without Search Trees
arXiv.org · 2019
30
cited
Hoplite: efficient and fault-tolerant collective communication for task-based distributed systems
Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication · 2020
28
cited
ESCHER: expressive scheduling with ephemeral resources
ACM Symposium on Cloud Computing · 2022
7
cited
SkyRL-Agent: Efficient RL Training for Multi-turn LLM Agent
arXiv.org · 2025
6
cited
Ray: A Distributed Execution Engine for the Machine Learning Ecosystem
2019
5
cited
Discriminating between causal structures in Bayesian Networks given partial observations
Kybernetika (Praha) · 2014
4
cited
Flexible Primitives for Distributed Deep Learning in Ray
2018
1
cited
Ray RLlib: A Framework for Distributed Reinforcement Learning
2017
1
cited
ARF-RLHF: Adaptive Reward-Following for RLHF through Emotion-Driven Self-Supervision and Trace-Biased Dynamic Optimization
Distributed Training for Reinforcement Learning
2020
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Philipp Moritz | Researcher Profile | Sotabase | Sotabase