Search Results for author: Patrick MacAlpine

Found 3 papers, 1 papers with code

Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL

1 code implementation • ICLR 2022 • Bogdan Mazoure, Ahmed M. Ahmed, Patrick MacAlpine, R Devon Hjelm, Andrey Kolobov

A highly desirable property of a reinforcement learning (RL) agent -- and a major difficulty for deep RL approaches -- is the ability to generalize policies learned on a few tasks over a high-dimensional observation space to similar tasks not seen during training.

Reinforcement Learning (RL) Representation Learning +1

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Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark

no code implementations • 29 Mar 2021 • Sharada Mohanty, Jyotish Poonganam, Adrien Gaidon, Andrey Kolobov, Blake Wulfe, Dipam Chakraborty, Gražvydas Šemetulskis, João Schapke, Jonas Kubilius, Jurgis Pašukonis, Linas Klimas, Matthew Hausknecht, Patrick MacAlpine, Quang Nhat Tran, Thomas Tumiel, Xiaocheng Tang, Xinwei Chen, Christopher Hesse, Jacob Hilton, William Hebgen Guss, Sahika Genc, John Schulman, Karl Cobbe

We present the design of a centralized benchmark for Reinforcement Learning which can help measure Sample Efficiency and Generalization in Reinforcement Learning by doing end to end evaluation of the training and rollout phases of thousands of user submitted code bases in a scalable way.

reinforcement-learning Reinforcement Learning (RL)

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Multi-Preference Actor Critic

no code implementations • 5 Apr 2019 • Ishan Durugkar, Matthew Hausknecht, Adith Swaminathan, Patrick MacAlpine

Policy gradient algorithms typically combine discounted future rewards with an estimated value function, to compute the direction and magnitude of parameter updates.

reinforcement-learning Reinforcement Learning (RL)

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