Search Results for author: Roberta Raileanu

Found 14 papers, 10 papers with code

Fast Adaptation to New Environments via Policy-Dynamics Value Functions

no code implementations ICML 2020 Roberta Raileanu, Max Goldstein, Arthur Szlam, Facebook Rob Fergus

An ensemble of conventional RL policies is used to gather experience on training environments, from which embeddings of both policies and environments can be learned.

Decoupling Value and Policy for Generalization in Reinforcement Learning

1 code implementation20 Feb 2021 Roberta Raileanu, Rob Fergus

Standard deep reinforcement learning algorithms use a shared representation for the policy and value function, especially when training directly from images.

reinforcement-learning

Fast Adaptation via Policy-Dynamics Value Functions

1 code implementation6 Jul 2020 Roberta Raileanu, Max Goldstein, Arthur Szlam, Rob Fergus

An ensemble of conventional RL policies is used to gather experience on training environments, from which embeddings of both policies and environments can be learned.

The NetHack Learning Environment

3 code implementations NeurIPS 2020 Heinrich Küttler, Nantas Nardelli, Alexander H. Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, Tim Rocktäschel

Here, we present the NetHack Learning Environment (NLE), a scalable, procedurally generated, stochastic, rich, and challenging environment for RL research based on the popular single-player terminal-based roguelike game, NetHack.

NetHack Score Systematic Generalization

RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments

3 code implementations ICLR 2020 Roberta Raileanu, Tim Rocktäschel

However, we show that existing methods fall short in procedurally-generated environments where an agent is unlikely to visit a state more than once.

Backplay: 'Man muss immer umkehren'

no code implementations ICLR 2019 Cinjon Resnick, Roberta Raileanu, Sanyam Kapoor, Alexander Peysakhovich, Kyunghyun Cho, Joan Bruna

Our contributions are that we analytically characterize the types of environments where Backplay can improve training speed, demonstrate the effectiveness of Backplay both in large grid worlds and a complex four player zero-sum game (Pommerman), and show that Backplay compares favorably to other competitive methods known to improve sample efficiency.

Backplay: "Man muss immer umkehren"

1 code implementation18 Jul 2018 Cinjon Resnick, Roberta Raileanu, Sanyam Kapoor, Alexander Peysakhovich, Kyunghyun Cho, Joan Bruna

Our contributions are that we analytically characterize the types of environments where Backplay can improve training speed, demonstrate the effectiveness of Backplay both in large grid worlds and a complex four player zero-sum game (Pommerman), and show that Backplay compares favorably to other competitive methods known to improve sample efficiency.

Modeling Others using Oneself in Multi-Agent Reinforcement Learning

1 code implementation ICML 2018 Roberta Raileanu, Emily Denton, Arthur Szlam, Rob Fergus

We consider the multi-agent reinforcement learning setting with imperfect information in which each agent is trying to maximize its own utility.

Multi-agent Reinforcement Learning reinforcement-learning

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