Search Results for author:
Found 7 papers, 4 papers with code
Learning the Reward Function for a Misspecified Model
Empirically, this approach to reward learning can yield dramatic improvements in control performance when the dynamics model is flawed.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents
The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge of building AI agents with general competency across dozens of Atari 2600 games.
Self-Correcting Models for Model-Based Reinforcement Learning
When an agent cannot represent a perfectly accurate model of its environment's dynamics, model-based reinforcement learning (MBRL) can fail catastrophically.
Model-based Reinforcement Learning
reinforcement-learning
+1
State of the Art Control of Atari Games Using Shallow Reinforcement Learning
The recently introduced Deep Q-Networks (DQN) algorithm has gained attention as one of the first successful combinations of deep neural networks and reinforcement learning.
Learning to Make Predictions In Partially Observable Environments Without a Generative Model
We formalize the problem of learning a prediction profile model as a transformation of the original model-learning problem, and show empirically that one can learn prediction profile models that make a small set of important predictions even in systems that are too complex for standard generative models.
Learning Partially Observable Models Using Temporally Abstract Decision Trees
This paper introduces timeline trees, which are partial models of partially observable environments.
Simple Local Models for Complex Dynamical Systems
We present a novel mathematical formalism for the idea of a local model,'' a model of a potentially complex dynamical system that makes only certain predictions in only certain situations.
