Search Results for author: Noor Sajid

Found 12 papers, 4 papers with code

Hierarchical generative modelling for autonomous robots

no code implementations15 Aug 2023 Kai Yuan, Noor Sajid, Karl Friston, Zhibin Li

We approach this problem by hierarchical generative modelling equipped with multi-level planning-for autonomous task completion-that mimics the deep temporal architecture of human motor control.

On efficient computation in active inference

1 code implementation2 Jul 2023 Aswin Paul, Noor Sajid, Lancelot Da Costa, Adeel Razi

Despite being recognized as neurobiologically plausible, active inference faces difficulties when employed to simulate intelligent behaviour in complex environments due to its computational cost and the difficulty of specifying an appropriate target distribution for the agent.

Bistable perception, precision and neuromodulation

no code implementations19 Dec 2022 Filip Novicky, Thomas Parr, Karl Friston, M. Berk Mirza, Noor Sajid

Bistable perception follows from observing a static, ambiguous, (visual) stimulus with two possible interpretations.

Bayesian Inference

Modelling non-reinforced preferences using selective attention

no code implementations25 Jul 2022 Noor Sajid, Panagiotis Tigas, Zafeirios Fountas, Qinghai Guo, Alexey Zakharov, Lancelot Da Costa

These memories are selectively attended to, using attention and gating blocks, to update agent's preferences.

OpenAI Gym

Active inference, Bayesian optimal design, and expected utility

no code implementations21 Sep 2021 Noor Sajid, Lancelot Da Costa, Thomas Parr, Karl Friston

Conversely, active inference reduces to Bayesian decision theory in the absence of ambiguity and relative risk, i. e., expected utility maximization.

Active Inference for Stochastic Control

1 code implementation27 Aug 2021 Aswin Paul, Noor Sajid, Manoj Gopalkrishnan, Adeel Razi

Active inference has emerged as an alternative approach to control problems given its intuitive (probabilistic) formalism.

Reinforcement Learning (RL)

Bayesian brains and the Rényi divergence

no code implementations12 Jul 2021 Noor Sajid, Francesco Faccio, Lancelot Da Costa, Thomas Parr, Jürgen Schmidhuber, Karl Friston

Under the Bayesian brain hypothesis, behavioural variations can be attributed to different priors over generative model parameters.

Bayesian Inference Variational Inference

Exploration and preference satisfaction trade-off in reward-free learning

no code implementations ICML Workshop URL 2021 Noor Sajid, Panagiotis Tigas, Alexey Zakharov, Zafeirios Fountas, Karl Friston

In this paper, we pursue the notion that this learnt behaviour can be a consequence of reward-free preference learning that ensures an appropriate trade-off between exploration and preference satisfaction.

OpenAI Gym

Reward Maximisation through Discrete Active Inference

no code implementations17 Sep 2020 Lancelot Da Costa, Noor Sajid, Thomas Parr, Karl Friston, Ryan Smith

Precisely, we show the conditions under which active inference produces the optimal solution to the Bellman equation--a formulation that underlies several approaches to model-based reinforcement learning and control.

Decision Making Model-based Reinforcement Learning +2

Deep active inference agents using Monte-Carlo methods

1 code implementation NeurIPS 2020 Zafeirios Fountas, Noor Sajid, Pedro A. M. Mediano, Karl Friston

In a more complex Animal-AI environment, our agents (using the same neural architecture) are able to simulate future state transitions and actions (i. e., plan), to evince reward-directed navigation - despite temporary suspension of visual input.

Active inference: demystified and compared

1 code implementation24 Sep 2019 Noor Sajid, Philip J. Ball, Thomas Parr, Karl J. Friston

In this paper, we provide: 1) an accessible overview of the discrete-state formulation of active inference, highlighting natural behaviors in active inference that are generally engineered in RL; 2) an explicit discrete-state comparison between active inference and RL on an OpenAI gym baseline.

Atari Games OpenAI Gym +2

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