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pycolab @ b389d1e

Attainable Utility Preservation

A test-bed for the Attainable Utility Preservation method for quantifying and penalizing the change an agent has on the world around it. This repository further augments this expansion to DeepMind's AI safety gridworlds. For discussion of AUP's potential contributions to long-term AI safety, see here.


  1. Using Python 2.7 as the interpreter, acquire the libraries in requirements.txt.
  2. Clone using --recursive to snag the pycolab submodule: git clone --recursive
  3. Run python -m experiments.charts or python -m experiments.ablation, tweaking the code to include the desired environments.


Our environments are Markov Decision Processes. All environments use a grid of size at most 10x10. Each cell in the grid can be empty, or contain a wall or other objects... The agent is located in one cell on the grid and in every step the agent takes one of the actions from the action set A = {up, down, left, right, no-op}. Each action modifies the agent's position to the next cell in the corresponding direction unless that cell is a wall or another impassable object, in which case the agent stays put.

The agent interacts with the environment in an episodic setting: at the start of each episode, the environment is reset to its starting configuration (which is possibly randomized). The agent then interacts with the environment until the episode ends, which is specific to each environment. We fix the maximal episode length to 20 steps. Several environments contain a goal cell... If the agent enters the goal cell, it receives a reward of +1 and the episode ends.

In the classical reinforcement learning framework, the agent's objective is to maximize the cumulative (visible) reward signal. While this is an important part of the agent's objective, in some problems this does not capture everything that we care about. Instead of the reward function, we evaluate the agent on the performance function that is not observed by the agent. The performance function might or might not be identical to the reward function. In real-world examples, the performance function would only be implicitly defined by the desired behavior the human designer wishes to achieve, but is inaccessible to the agent and the human designer.







The Conveyor-Sushi variant induces similar behavior:

Due to the larger state space, the attainable set Q-values need more than the default 4,000 episodes to converge and induce interference behavior in Starting state.


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