Changing the Environment Based on Empowerment as Intrinsic Motivation
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Abstract (arXiv)
One aspect of intelligence is the ability to restructure your own environment so that the world you live in becomes more beneficial to you. In this paper we investigate how the information-theoretic measure of agent empowerment can provide a task-independent, intrinsic motivation to restructure the world. We show how changes in embodiment and in the environment change the resulting behaviour of the agent and the artefacts left in the world. For this purpose, we introduce an approximation of the established empowerment formalism based on sparse sampling, which is simpler and significantly faster to compute for deterministic dynamics. Sparse sampling also introduces a degree of randomness into the decision making process, which turns out to beneficial for some cases. We then utilize the measure to generate agent behaviour for different agent embodiments in a Minecraft-inspired three dimensional block world. The paradigmatic results demonstrate that empowerment can be used as a suitable generic intrinsic motivation to not only generate actions in given static environments, as shown in the past, but also to modify existing environmental conditions. In doing so, the emerging strategies to modify an agent's environment turn out to be meaningful to the specific agent capabilities, i.e., de facto to its embodiment.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jun 2014.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 4; 2026: 2.
9 papers.
- 2022Choreographer: Learning and Adapting Skills in Imaginationcited by 31
- 2021Explore and Control with Adversarial Surprisecited by 6
- 2025Intrinsically-Motivated Humans and Agents in Open-World Explorationcited by 6
- 2024BAMDP Shaping: a Unified Framework for Intrinsic Motivation and Reward Shapingcited by 4
- 2025Decentralized Traffic Flow Optimization Through Intrinsic Motivationcited by 1
- 2025Information-Theoretic Policy Pre-Training with Empowermentcited by 1
- 2026Artificial Agency Program: Curiosity, compression, and communication in agentscited by 1
- 2025Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learningcited by 0
- 2026C-STEP: Continuous Space-Time Empowerment for Physics-informed Safe Reinforcement Learning of Mobile Agentscited by 0
- 2026C-STEP: Continuous Space-Time Empowerment for Physics-informed Safe Reinforcement Learning of Mobile Agents25 Mar 2026
- 2026Artificial Agency Program: Curiosity, compression, and communication in agents27 Feb 2026
- 2025Information-Theoretic Policy Pre-Training with Empowerment7 Oct 2025
- 2025Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning13 May 2025
- 2025Decentralized Traffic Flow Optimization Through Intrinsic Motivation8 May 2025
- 2025Intrinsically-Motivated Humans and Agents in Open-World Exploration31 Mar 2025
- 2024BAMDP Shaping: a Unified Framework for Intrinsic Motivation and Reward Shaping9 Sep 2024
- 2022Choreographer: Learning and Adapting Skills in Imagination23 Nov 2022
- 2021Explore and Control with Adversarial Surprise12 Jul 2021