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Found 7 papers, 0 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.
Work in Progress: Temporally Extended Auxiliary Tasks
Our overall conclusions are that TD-AE increases the robustness of the A2C algorithm to the trajectory length and while promising, further study is required to fully understand the relationship between auxiliary task prediction timescale and the agent's performance.
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.
Accelerating Learning in Constructive Predictive Frameworks with the Successor Representation
As a primary contribution of this work, we show that using SR-based predictions can improve sample efficiency and learning speed in a continual learning setting where new predictions are incrementally added and learned over time.
Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods
This paper investigates estimating the variance of a temporal-difference learning agent's update target.
Communicative Capital for Prosthetic Agents
This work presents an overarching perspective on the role that machine intelligence can play in enhancing human abilities, especially those that have been diminished due to injury or illness.
Introspective Agents: Confidence Measures for General Value Functions
Agents of general intelligence deployed in real-world scenarios must adapt to ever-changing environmental conditions.
