Efficiently learns object representations for FPS games.
problem Learning to play FPS games with limited training data.
method Detects salient segments, clusters them, and uses their importance for classification.
result Improves performance of DRQN by focusing on relevant object categories.
GANs generate DOOM levels similar to human-designed ones.
problem Generating levels similar to human-designed ones in first-person shooter games.
method Extracted features from human-designed levels, trained GANs on these features and level images, generated new levels, compared results.
result GANs can generate levels similar to human-designed ones.
Paper proposes a more robust deep reinforcement learning agent.
problem Creating more robust reinforcement learning agents.
method Modified A3C algorithm with dual input streams.
result Significant reduction in training parameters (30%) with improved robustness.
ViZDoom competition tests AI in Doom using only visual input.
problem Creating AI bots that play Doom effectively from raw visual data.
method Multi-player deathmatch in Doom; bots make decisions based on screen buffer.
result Reinforcement learning can produce capable bots but not competitive against humans.
The paper investigates the effectiveness of reusing experience in Deep Q-Learning for FPS environments.
problem The high number of interactions required for reinforcement learning limits its practicality.
method The authors test the effectiveness of applying learning update steps multiple times per environmental step in the VizDoom environment.
result Updating learning steps less frequently than every 4th environmental step does not improve performance and can degrade performance.
Deep RL agent performs well in Doom, a complex FPS game.
problem Addressing complex environments with sparse rewards and large state spaces.
method Divide and conquer approach using unsupervised auxiliary tasks.
result Our agent performs better in unknown environments than state-of-the-art algorithms.
AI agent plays CSGO deathmatch with human-like style.
problem Lack of API for CSGO limits data for reinforcement learning.
method Behavioural cloning on large noisy and expert datasets.
result Matches human difficulty level in deathmatch mode.
Neural MMO simulates MMOs to study multiagent intelligence.
problem Limited research environments for multiagent intelligence.
method Developed a new game environment inspired by MMOs.
result Standard methods can learn interesting behaviors in MMOs.
High-throughput 3D control training system achieves 100,000 FPS.
problem Lack of efficient, single-machine reinforcement learning systems.
method Sample Factory combines asynchronous sampling and off-policy correction.
result Achieves 100,000 FPS on 3D control problems without sacrificing sample efficiency.
UAVs use RL to outperform a greedy shooter in 2D engagements.
problem Optimizing UAV pursuit-evasion strategies in 2D environments.
method Reinforcement learning applied to a greedy shooter strategy in a 2D aerial engagement.
result Deep Q-learning UAV outperforms greedy shooter UAV in simulations.
WILD-SCAV benchmarks AI in complex 3D FPS environments.
problem Lack of complexity and diversity in RL environments.
method Developed a 3D open-world FPS game environment.
result Demonstrates effectiveness in benchmarking RL algorithms.
AppStreamer reduces mobile game storage by predicting needed files.
problem Expanding storage needs of mobile games and apps.
method Predictive streaming of app files from cloud or edge servers.
result Reduces storage by 87% for Dead Effect 2 and 86% for Fire Emblem Heroes.
Agents learn to play a first-person multiplayer game at human level performance.
problem Training AI agents for complex, multi-agent, real-time environments.
method Population-based deep reinforcement learning with concurrent training of multiple agents.
result Achieved human-level performance in a first-person multiplayer game.
Flatland is a simple 2D environment for reinforcement learning.
problem Testing and prototyping reinforcement learning agents.
method Simple, lightweight, customizable environment with physical properties.
result Baseline reinforcement learning agents can solve a navigation task in Flatland.
Study how actions affect perception in embodied agents using group theory.
problem Understanding how actions influence perception in autonomous agents.
method Mathematical formalism of group theory applied to sensory commutativity of action sequences.
result Introduced Sensory Commutativity Probability (SCP) to measure action effects on perception.
We explore the perspective of a bug living on the two-dimensional surface of a polyhedron. Images of various kinds of effects like lensing and cloaking are shown via color pictures of three viewpoints: the first person perspective of the bug, a map of the bug's viewpoint, and a look at the bug on the embedded polyhedro…
Model-based deep reinforcement learning improves Minecraft task performance.
problem Optimizing performance in Minecraft block-placing tasks.
method Combining DNN-based transition model with Monte Carlo tree search.
result Model-based approach achieves comparable performance to model-free methods but learns faster.
Agent learns to read maps and navigate mazes using deep reinforcement learning.
problem Teaching a machine to understand and navigate 3D environments from 2D maps.
method Combines A3C with a recurrent localization cell, learns localization from 3D images.
result Agent successfully navigates and localizes in random mazes, generalizing to larger mazes.
New approach handles stochastic and partially-observable environments using discrete autoencoders and Monte Carlo tree search.
problem Challenges in planning for stochastic and partially-observable environments.
method Uses discrete autoencoders and a stochastic variant of Monte Carlo tree search.
result Significantly outperforms MuZero on stochastic chess and scales to DeepMind Lab.
Gated Path Planning Network improves path planning efficiency and stability.
problem Training instability and optimization issues in Value Iteration Networks.
method Reframed VINs as recurrent-convolutional networks with gated update equations.
result Gated Path Planning Network outperforms VINs in various metrics.
A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions. However when employed in complex 3D environments, they typically suffer from challenges related to partial observability, combinatorial exploration spaces, path planning, and a scarc…
Method learns representations invariant to task-irrelevant details in reinforcement learning tasks.
problem Learning representations that are invariant to task-irrelevant details in reinforcement learning.
method Uses bisimulation metrics to learn robust latent representations that encode only task-relevant information.
result Demonstrates SOTA performance in modified visual MuJoCo tasks and a first-person driving task.
Paper tackles activity recognition from body-worn video footage.
problem Classifying frames of body-worn video footage according to the wearer's activity.
method Extract motion features and semi-supervised classification.
result Method achieves comparable results to supervised and deep learning methods using less training data.
A neural network predicts a body image from sensorimotor data.
problem How to acquire a body image from sensorimotor data.
method A two-branches deconvolutional neural network trained on first-person images.
result The network can automatically isolate the visible arm from the environment.
Agent learns third-person manipulation tasks from a single video.
problem Learning from third-person videos to perform novel tasks.
method Decoupling high-level task generation from low-level action prediction.
result Agent successfully learns and performs tasks in unseen scenarios.
Georg de Buquoy, Lord de Vaux, lived in Nove Hrady, Prague and Cerveny Hradek for most of his productive life. From his extensive scientific contributions, both theoretical and experimental, we expand here the discussion of his contributions to mathematical economy. He is mainly celebrated as the first persons to defin…
Mix&Match framework speeds up RL agent training by bootstrapping from simpler agents.
problem Training complex reinforcement learning agents is slow and challenging.
method Automatically forms a curriculum over agents, progressively training more complex agents.
result Significant performance gains in diverse experimental setups.
USFAs combine UVFAs, SFs, and GPI for scalable, instant RL generalisation.
problem Generalizing to unseen tasks in reinforcement learning.
method Combining universal value function approximators, successor features, and generalized policy improvement.
result Demonstrates practical benefits and transfer abilities in a complex 3D environment.
A new curiosity method uses episodic memory to reward novelty, improving learning from sparse rewards.
problem Sparse rewards in real-world reinforcement learning.
method Uses episodic memory to form novelty bonuses based on reachability.
result Improves learning from sparse rewards in various environments.
Hyperelastic bodies in Riemannian manifolds can levitate due to curvature-induced forces.
problem Hyperelastic bodies in Riemannian manifolds can levitate due to curvature-induced forces.
method Numerical simulations of static solutions to a particular class of problems in hyperelastic mechanics.
result Hyperelastic bodies in Riemannian manifolds can levitate due to curvature-induced forces.