Reward Maximisation through Discrete Active Inference

• URL: http://arxiv.org/abs/2009.08111v4
• Date: Mon, 11 Jul 2022 19:58:29 GMT
• Title: Reward Maximisation through Discrete Active Inference
• Authors: Lancelot Da Costa, Noor Sajid, Thomas Parr, Karl Friston, Ryan Smith
• Abstract summary: We show how and when active inference agents perform actions that are optimal for maximising reward. We show the conditions under which active inference produces the optimal solution to the Bellman equation. We append the analysis with a discussion of the broader relationship between active inference and reinforcement learning.
• Score: 1.2074552857379273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Active inference is a probabilistic framework for modelling the behaviour of biological and artificial agents, which derives from the principle of minimising free energy. In recent years, this framework has successfully been applied to a variety of situations where the goal was to maximise reward, offering comparable and sometimes superior performance to alternative approaches. In this paper, we clarify the connection between reward maximisation and active inference by demonstrating how and when active inference agents perform actions that are optimal for maximising reward. 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. On partially observed Markov decision processes, the standard active inference scheme can produce Bellman optimal actions for planning horizons of 1, but not beyond. In contrast, a recently developed recursive active inference scheme (sophisticated inference) can produce Bellman optimal actions on any finite temporal horizon. We append the analysis with a discussion of the broader relationship between active inference and reinforcement learning.

Related papers

• Demonstrating the Continual Learning Capabilities and Practical Application of Discrete-Time Active Inference [0.0]
  Active inference is a mathematical framework for understanding how agents interact with their environments. In this paper, we present a continual learning framework for agents operating in discrete time environments. We demonstrate the agent's ability to relearn and refine its models efficiently, making it suitable for complex domains like finance and healthcare.
  arXiv  Detail & Related papers  (2024-09-30T21:18:46Z)
• Generative Intrinsic Optimization: Intrinsic Control with Model Learning [5.439020425819001]
  Future sequence represents the outcome after executing the action into the environment. Explicit outcomes may vary across state, return, or trajectory serving different purposes such as credit assignment or imitation learning. We propose a policy scheme that seamlessly incorporates the mutual information, ensuring convergence to the optimal policy.
  arXiv  Detail & Related papers  (2023-10-12T07:50:37Z)
• Reinforcement Learning with Non-Cumulative Objective [12.906500431427716]
  In reinforcement learning, the objective is almost always defined as a emphcumulative function over the rewards along the process. In this paper, we propose a modification to existing algorithms for optimizing such objectives.
  arXiv  Detail & Related papers  (2023-07-11T01:20:09Z)
• When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning [62.00672284480755]
  This paper aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving.
  arXiv  Detail & Related papers  (2023-02-15T04:14:20Z)
• Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes [8.710154439846816]
  We consider a sequential decision making problem where the agent faces the environment characterized by discrete events. This problem exists ubiquitously in social media, finance and health informatics but is rarely investigated by the conventional research in reinforcement learning. We present a novel framework of model-based reinforcement learning where the agent's actions and observations are asynchronous discrete events occurring in continuous-time.
  arXiv  Detail & Related papers  (2022-01-29T11:53:40Z)
• Active inference, Bayesian optimal design, and expected utility [1.433758865948252]
  We describe how active inference combines Bayesian decision theory and optimal Bayesian design principles to minimize expected free energy. It is this aspect of active inference that allows for the natural emergence of information-seeking behavior. Our Tmaze simulations show optimizing expected free energy produces goal-directed information-seeking behavior while optimizing expected utility induces purely exploitive behavior.
  arXiv  Detail & Related papers  (2021-09-21T20:56:32Z)
• APS: Active Pretraining with Successor Features [96.24533716878055]
  We show that by reinterpreting and combining successorcitepHansenFast with non entropy, the intractable mutual information can be efficiently optimized. The proposed method Active Pretraining with Successor Feature (APS) explores the environment via non entropy, and the explored data can be efficiently leveraged to learn behavior.
  arXiv  Detail & Related papers  (2021-08-31T16:30:35Z)
• Outcome-Driven Reinforcement Learning via Variational Inference [95.82770132618862]
  We discuss a new perspective on reinforcement learning, recasting it as the problem of inferring actions that achieve desired outcomes, rather than a problem of maximizing rewards. To solve the resulting outcome-directed inference problem, we establish a novel variational inference formulation that allows us to derive a well-shaped reward function. We empirically demonstrate that this method eliminates the need to design reward functions and leads to effective goal-directed behaviors.
  arXiv  Detail & Related papers  (2021-04-20T18:16:21Z)
• Efficient Empowerment Estimation for Unsupervised Stabilization [75.32013242448151]
  empowerment principle enables unsupervised stabilization of dynamical systems at upright positions. We propose an alternative solution based on a trainable representation of a dynamical system as a Gaussian channel. We show that our method has a lower sample complexity, is more stable in training, possesses the essential properties of the empowerment function, and allows estimation of empowerment from images.
  arXiv  Detail & Related papers  (2020-07-14T21:10:16Z)
• Control as Hybrid Inference [62.997667081978825]
  We present an implementation of CHI which naturally mediates the balance between iterative and amortised inference. We verify the scalability of our algorithm on a continuous control benchmark, demonstrating that it outperforms strong model-free and model-based baselines.
  arXiv  Detail & Related papers  (2020-07-11T19:44:09Z)
• Reinforcement Learning through Active Inference [62.997667081978825]
  We show how ideas from active inference can augment traditional reinforcement learning approaches. We develop and implement a novel objective for decision making, which we term the free energy of the expected future. We demonstrate that the resulting algorithm successfully exploration and exploitation, simultaneously achieving robust performance on several challenging RL benchmarks with sparse, well-shaped, and no rewards.
  arXiv  Detail & Related papers  (2020-02-28T10:28:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.