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Found 8 papers, 0 papers with code
Five Properties of Specific Curiosity You Didn't Know Curious Machines Should Have
As a second main contribution of this work, we show how these properties may be implemented together in a proof-of-concept reinforcement learning agent: we demonstrate how the properties manifest in the behaviour of this agent in a simple non-episodic grid-world environment that includes curiosity-inducing locations and induced targets of curiosity.
What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience
In computational reinforcement learning, a growing body of work seeks to construct an agent's perception of the world through predictions of future sensations; predictions about environment observations are used as additional input features to enable better goal-directed decision-making.
Prototyping three key properties of specific curiosity in computational reinforcement learning
Curiosity for machine agents has been a focus of intense research.
Finding Useful Predictions by Meta-gradient Descent to Improve Decision-making
In computational reinforcement learning, a growing body of work seeks to express an agent's model of the world through predictions about future sensations.
Affordance as general value function: A computational model
General value functions (GVFs) in the reinforcement learning (RL) literature are long-term predictive summaries of the outcomes of agents following specific policies in the environment.
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.
Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures
Predictions and predictive knowledge have seen recent success in improving not only robot control but also other applications ranging from industrial process control to rehabilitation.
Interpretable PID Parameter Tuning for Control Engineering using General Dynamic Neural Networks: An Extensive Comparison
In this paper, we examine the utility of extending PID controllers with recurrent neural networks-namely, General Dynamic Neural Networks (GDNN); we show that GDNN (neural) PID controllers perform well on a range of control systems and highlight how they can be a scalable and interpretable option for control systems.
