40 citations · 40 across the 2 of their papers we have counts for
5 papers
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…