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Found 7 papers, 1 papers with code
Value Function Decomposition for Iterative Design of Reinforcement Learning Agents
These value estimates provide insight into an agent's learning and decision-making process and enable new training methods to mitigate common problems.
Gamma-Nets: Generalizing Value Estimation over Timescale
By using the timescale as one of the estimator's inputs we can estimate value for arbitrary timescales.
Implementing the Deep Q-Network
The Deep Q-Network proposed by Mnih et al. [2015] has become a benchmark and building point for much deep reinforcement learning research.
Environment-Independent Task Specifications via GLTL
We propose a new task-specification language for Markov decision processes that is designed to be an improvement over reward functions by being environment independent.
Interactive Learning from Policy-Dependent Human Feedback
This paper investigates the problem of interactively learning behaviors communicated by a human teacher using positive and negative feedback.
Showing versus doing: Teaching by demonstration
Stark differences arise when demonstrators are intentionally teaching a task versus simply performing a task.
