40 citations · 81 across the 10 of their papers we have counts for
10 papers
Smaller Abstract State Spaces Enable Cross-Scale Generalization in Reinforcement Learning
Nasehatul Mustakim, Lucas Lehnert
While humans readily generalize abstract concepts to more complex or larger tasks, building Reinforcement Learning (RL) systems with this ability remains elusive. Here, we present…
Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping
Lucas Lehnert, Sainbayar Sukhbaatar, DiJia Su +4
While Transformers have enabled tremendous progress in various application settings, such architectures still trail behind traditional symbolic planners for solving complex decisio…
Maximum State Entropy Exploration using Predecessor and Successor Representations
Arnav Kumar Jain, Lucas Lehnert, Irina Rish +1
Animals have a developed ability to explore that aids them in important tasks such as locating food, exploring for shelter, and finding misplaced items. These exploration skills ne…
IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control
Rohan Chitnis, Yingchen Xu, Bobak Hashemi +4
Model-based reinforcement learning (RL) has shown great promise due to its sample efficiency, but still struggles with long-horizon sparse-reward tasks, especially in offline setti…
Reward-Predictive Clustering
Lucas Lehnert, Michael J. Frank, Michael L. Littman
Recent advances in reinforcement-learning research have demonstrated impressive results in building algorithms that can out-perform humans in complex tasks. Nevertheless, creating…
Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning
Lucas Lehnert, Michael L. Littman
A key question in reinforcement learning is how an intelligent agent can generalize knowledge across different inputs. By generalizing across different inputs, information learned…
Mitigating Planner Overfitting in Model-Based Reinforcement Learning
Dilip Arumugam, David Abel, Kavosh Asadi +5
An agent with an inaccurate model of its environment faces a difficult choice: it can ignore the errors in its model and act in the real world in whatever way it determines is opti…
Transfer with Model Features in Reinforcement Learning
Lucas Lehnert, Michael L. Littman
A key question in Reinforcement Learning is which representation an agent can learn to efficiently reuse knowledge between different tasks. Recently the Successor Representation wa…
Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning
Lucas Lehnert, Stefanie Tellex, Michael L. Littman
One question central to Reinforcement Learning is how to learn a feature representation that supports algorithm scaling and re-use of learned information from different tasks. Succ…
Policy Gradient Methods for Off-policy Control
Lucas Lehnert, Doina Precup
Off-policy learning refers to the problem of learning the value function of a way of behaving, or policy, while following a different policy. Gradient-based off-policy learning alg…