Behavioural Cloning in VizDoom

• URL: http://arxiv.org/abs/2401.03993v1
• Date: Mon, 8 Jan 2024 16:15:43 GMT
• Title: Behavioural Cloning in VizDoom
• Authors: Ryan Spick, Timothy Bradley, Ayush Raina, Pierluigi Vito Amadori, Guy Moss
• Abstract summary: This paper describes methods for training autonomous agents to play the game "Doom 2" through Imitation Learning (IL) We also explore how Reinforcement Learning (RL) compares to IL for humanness by comparing camera movement and trajectory data.
• Score: 1.4999444543328293
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper describes methods for training autonomous agents to play the game "Doom 2" through Imitation Learning (IL) using only pixel data as input. We also explore how Reinforcement Learning (RL) compares to IL for humanness by comparing camera movement and trajectory data. Through behavioural cloning, we examine the ability of individual models to learn varying behavioural traits. We attempt to mimic the behaviour of real players with different play styles, and find we can train agents that behave aggressively, passively, or simply more human-like than traditional AIs. We propose these methods of introducing more depth and human-like behaviour to agents in video games. The trained IL agents perform on par with the average players in our dataset, whilst outperforming the worst players. While performance was not as strong as common RL approaches, it provides much stronger human-like behavioural traits to the agent.

Related papers

• Toward Human-AI Alignment in Large-Scale Multi-Player Games [24.784173202415687]
  We analyze extensive human gameplay data from Xbox's Bleeding Edge (100K+ games) We find that while human players exhibit variability in fight-flight and explore-exploit behavior, AI players tend towards uniformity. These stark differences underscore the need for interpretable evaluation, design, and integration of AI in human-aligned applications.
  arXiv  Detail & Related papers  (2024-02-05T22:55:33Z)
• Enhancing Human Experience in Human-Agent Collaboration: A Human-Centered Modeling Approach Based on Positive Human Gain [18.968232976619912]
  We propose a "human-centered" modeling scheme for collaborative AI agents. We expect that agents should learn to enhance the extent to which humans achieve these goals while maintaining agents' original abilities. We evaluate the RLHG agent in the popular Multi-player Online Battle Arena (MOBA) game, Honor of Kings.
  arXiv  Detail & Related papers  (2024-01-28T05:05:57Z)
• Learning to Influence Human Behavior with Offline Reinforcement Learning [70.7884839812069]
  We focus on influence in settings where there is a need to capture human suboptimality. Experiments online with humans is potentially unsafe, and creating a high-fidelity simulator of the environment is often impractical. We show that offline reinforcement learning can learn to effectively influence suboptimal humans by extending and combining elements of observed human-human behavior.
  arXiv  Detail & Related papers  (2023-03-03T23:41:55Z)
• Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback [16.268581985382433]
  An important goal in artificial intelligence is to create agents that can both interact naturally with humans and learn from their feedback. Here we demonstrate how to use reinforcement learning from human feedback to improve upon simulated, embodied agents.
  arXiv  Detail & Related papers  (2022-11-21T16:00:31Z)
• Generative Personas That Behave and Experience Like Humans [3.611888922173257]
  generative AI agents attempt to imitate particular playing behaviors represented as rules, rewards, or human demonstrations. We extend the notion of behavioral procedural personas to cater for player experience, thus examining generative agents that can both behave and experience their game as humans would. Our findings suggest that the generated agents exhibit distinctive play styles and experience responses of the human personas they were designed to imitate.
  arXiv  Detail & Related papers  (2022-08-26T12:04:53Z)
• Explaining Deep Reinforcement Learning Agents In The Atari Domain through a Surrogate Model [78.69367679848632]
  We describe a lightweight and effective method to derive explanations for deep RL agents. Our method relies on a transformation of the pixel-based input of the RL agent to an interpretable, percept-like input representation. We then train a surrogate model, which is itself interpretable, to replicate the behavior of the target, deep RL agent.
  arXiv  Detail & Related papers  (2021-10-07T05:01:44Z)
• Predicting Game Engagement and Difficulty Using AI Players [3.0501851690100277]
  This paper presents a novel approach to automated playtesting for the prediction of human player behavior and experience. It has previously been demonstrated that Deep Reinforcement Learning game-playing agents can predict both game difficulty and player engagement. We improve this approach by enhancing DRL with Monte Carlo Tree Search (MCTS)
  arXiv  Detail & Related papers  (2021-07-26T09:31:57Z)
• PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training [94.87393610927812]
  We present an off-policy, interactive reinforcement learning algorithm that capitalizes on the strengths of both feedback and off-policy learning. We demonstrate that our approach is capable of learning tasks of higher complexity than previously considered by human-in-the-loop methods.
  arXiv  Detail & Related papers  (2021-06-09T14:10:50Z)
• Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data [70.540936204654]
  "Learning to run" competition was to train a two-legged model of a humanoid body to run in a simulated race course with maximum speed. All submissions took a tabula rasa approach to reinforcement learning (RL) and were able to produce relatively fast, but not optimal running behaviour. We demonstrate how data from videos of human running can be used to shape the reward of the humanoid learning agent.
  arXiv  Detail & Related papers  (2020-12-16T09:46:58Z)
• Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card Game [71.24825724518847]
  We present a study on how popular reinforcement learning algorithms can be adapted to learn and to play a real-world implementation of a competitive multiplayer card game. We propose specific training and validation routines for the learning agents, in order to evaluate how the agents learn to be competitive and explain how they adapt to each others' playing style.
  arXiv  Detail & Related papers  (2020-04-08T14:11:05Z)
• On the interaction between supervision and self-play in emergent communication [82.290338507106]
  We investigate the relationship between two categories of learning signals with the ultimate goal of improving sample efficiency. We find that first training agents via supervised learning on human data followed by self-play outperforms the converse.
  arXiv  Detail & Related papers  (2020-02-04T02:35:19Z)

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.