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Found 12 papers, 0 papers with code
Small Dataset, Big Gains: Enhancing Reinforcement Learning by Offline Pre-Training with Model Based Augmentation
Offline reinforcement learning leverages pre-collected datasets of transitions to train policies.
Improving Generalization in Game Agents with Data Augmentation in Imitation Learning
Imitation learning is an effective approach for training game-playing agents and, consequently, for efficient game production.
Generating Personas for Games with Multimodal Adversarial Imitation Learning
MultiGAIL is based on generative adversarial imitation learning and uses multiple discriminators as reward models, inferring the environment reward by comparing the agent and distinct expert policies.
Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Games
Going from research to production, especially for large and complex software systems, is fundamentally a hard problem.
Efficient Ground Vehicle Path Following in Game AI
This short paper presents an efficient path following solution for ground vehicles tailored to game AI.
Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning
In games, as in and many other domains, design validation and testing is a huge challenge as systems are growing in size and manual testing is becoming infeasible.
Contextual Decision Trees
Focusing on Random Forests, we propose a multi-armed contextual bandit recommendation framework for feature-based selection of a single shallow tree of the learned ensemble.
CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories
This paper proposes a novel deep reinforcement learning algorithm to perform automatic analysis and detection of gameplay issues in complex 3D navigation environments.
Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents
To this end, we propose four different policy fusion methods for combining pre-trained policies.
Deep Policy Networks for NPC Behaviors that Adapt to Changing Design Parameters in Roguelike Games
Recent advances in Deep Reinforcement Learning (DRL) have largely focused on improving the performance of agents with the aim of replacing humans in known and well-defined environments.
Demonstration-efficient Inverse Reinforcement Learning in Procedurally Generated Environments
We propose a technique based on Adversarial Inverse Reinforcement Learning which can significantly decrease the need for expert demonstrations in PCG games.
DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games
In this paper we introduce DeepCrawl, a fully-playable Roguelike prototype for iOS and Android in which all agents are controlled by policy networks trained using Deep Reinforcement Learning (DRL).
