Capsule networks improve AI in complex game environments.
problem Improving AI opponents in advanced game environments.
method Introducing four new game environments, generating training data, and applying CapsNet for Deep Q-Learning.
result CapsNet is a reliable architecture for game AI.
Google Research Football: A new 3D physics-based game for reinforcement learning.
problem Training reinforcement learning algorithms in complex, realistic environments.
method Developed a new 3D physics-based football simulator environment.
result Reported baseline results for various reinforcement algorithms.
Improved model predicts future environment changes efficiently.
problem Efficiently predicting future changes in environments for agents.
method Recurrent neural networks for high-dimensional pixel observations, reducing computational load.
result Model can predict hundreds of time-steps into the future, improving exploration and adaptability.
Hanabi game challenges AI with cooperative, imperfect information.
problem Developing AI capable of understanding others' beliefs and intentions.
method Introducing the Hanabi Learning Environment and experimental framework.
result Hanabi highlights the importance of theory of mind reasoning in AI.
A new method simulates large, diverse populations of learning agents evolving in games.
problem Limited scalability and efficiency of Multi-Agent Reinforcement Learning.
method Parallelizable implementation of Policy Gradient and Opponent-Learning Awareness for evolutionary simulations.
result Simulated large, diverse populations of learning agents evolve under various strategies.
AEC Games model represents software MARL environments better than POSGs.
problem POSGs are conceptually unsuitable for software MARL environments.
method Introduced AEC Games model as an equivalent to POSGs.
result AEC Games model is more representative of software MARL environments.
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.
Unity offers a flexible platform for AI research with rich simulations.
problem Limited configurability and complexity in existing simulation environments.
method Proposed a taxonomy of simulation platforms and demonstrated Unity's suitability.
result Unity enables rich, complex simulations for AI research.
This paper shows how rotating, cropping, and translating images improves a reinforcement learning agent's ability to generalize.
problem Reinforcement learning agents struggle to generalize to slight variations of their training environments.
method The authors investigate the impact of rotation, translation, and cropping on the input representation of reinforcement learning agents.
result Cropped, translated, and rotated observations lead to better generalization in reinforcement learning agents.
RL benefits from natural language understanding, surveying recent advances.
problem Exploit natural language to enhance RL performance.
method Survey recent research integrating natural language with RL.
result Natural language can improve RL's world knowledge and task transfer.
Optimistic Thompson Sampling reduces regret in unknown multi-player games.
problem Navigating uncertainty in unknown multi-player games with strategic decision-making.
method Introduces Thompson Sampling algorithms that exploit opponents' actions and reward structures.
result Achieves over tenfold improvements in experimental budgets with logarithmic regret bound.
Imaginative RL uses GANs to simulate real environments, making RL more efficient.
problem Lack of data efficiency and safety constraints in reinforcement learning.
method Generative Adversarial Imaginative Reinforcement Learning (GAIL) algorithm.
result The proposed algorithm more efficiently utilizes real-world experience.
Decomposes game environments into controllable and non-controllable parts for faster learning.
problem Difficulties in end-to-end learning for complex environments.
method Modularization of the environment into controllable and non-controllable parts, using supervised and reinforcement learning.
result Human-level performance achieved in 10-15 minutes of game time with proper decomposition.
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.
New algorithms achieve logarithmic regret in KL-regularized Markov games.
problem Improving sample efficiency in game-theoretic settings with KL regularization.
method Developed OMG and SOMG algorithms for matrix and Markov games, using best response sampling and superoptimistic bonuses.
result Logarithmic regret in T that scales inversely with KL regularization strength β. Deep RL tested on combinatorial games from Erdos et al.
problem Evaluate reinforcement learning algorithms on challenging combinatorial games.
method Use Erdos-Selfridge-Spencer games with known optimal solutions.
result Demonstrates strengths and limitations of current RL approaches.
A human-like strategy improves DRL performance in Atari games.
problem Lack of exploration in DRL for high-dimensional problems.
method Mixed strategy approach mimicking human behavior.
result Higher maximum score probability achieved in Breakout game.
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.
Deep learning predicts both video frames and rewards in Atari games.
problem Efficiently predicting rewards in complex, unknown environments.
method Jointly optimizing video frame and reward prediction using a deep neural network.
result Accurate cumulative reward prediction up to 200 frames.
A game environment simulates competition among many agents for resources.
problem Understanding large-scale multiagent interactions and resource competition.
method Developed a persistent, massively multiplayer AI environment.
result Population size affects the development of skillful behaviors and niche differentiation.
PettingZoo library accelerates multi-agent reinforcement learning research.
problem Challenges in multi-agent reinforcement learning, especially conceptual models of games.
method Developed PettingZoo library with AEC games model to address multi-agent reinforcement learning challenges.
result AEC games model addresses conceptual issues in multi-agent reinforcement learning environments.
Study shows curiosity-driven learning can perform well without extrinsic rewards.
problem Lack of scalable methods for intrinsic reward design in reinforcement learning.
method Performed a large-scale study of curiosity-driven learning across 54 environments, using prediction error as reward.
result Curiosity-driven learning can achieve good performance without extrinsic rewards, aligning with hand-designed rewards in many cases.
TextWorld is a Python library for RL agents in text-based games.
problem Training RL agents on text-based games with varying challenges and sparse rewards.
method Developed a Python library with backend functions for state tracking and reward assignment. Enables users to create new games with precise control over difficulty and scope.
result Demonstrated the effectiveness of TextWorld in training RL agents on a curated list of games and generated sets of games.
CNN-AIRL improves video game performance using expert demonstrations.
problem Designing reward functions for complex video games is difficult.
method Adversarial IRL with CNNs, autoencoder state representation, reward normalization.
result Achieves high-level performance on Catcher game, outperforming baseline.
Fractal neural networks play SimCity and Conway's Game of Life on varying scales.
problem Generalizing agents' performance to larger gameboards than during training.
method Reinforcement learning in a custom environment, using fractal neural networks.
result Agents can generalize to larger gameboards, solving a minigame unsolvable with local strategies.
Deep RL algorithms tested on diverse video games.
problem Benchmarking AI on diverse video games.
method Interface GVGAI to OpenAI Gym, test deep RL algorithms.
result Characterized performance of deep RL algorithms on GVGAI games.
Study online and offline social interactions using game theory.
problem Effects of online social networking on offline interactions and collective well-being.
method Evolutionary game theory approach to model socialization strategies.
result Self-protective behaviors can lead to non-socially optimal outcomes.
LeDeepChef learns to play multiple cooking games well.
problem Designing a general RL agent for multiple games of the same family.
method Actor-critic framework, action-space pruning, hierarchical RL, specialized module.
result LeDeepChef outperformed competitors on a diverse set of cooking games.
New method finds invariant predictors to improve model robustness.
problem Machine learning models are brittle in new environments with spurious correlations.
method Formulates invariant risk minimization as a Nash equilibrium game.
result Algorithm yields similar or better accuracy with lower variance.
The paper analyzes trade execution strategies for large traders in a stochastic market environment.
problem Analyzing trade execution strategies in a stochastic market with price impact.
method Formulated a Markov game model and used backward induction method of dynamic programming.
result Explicit closed-form execution strategy at Markov perfect equilibrium.
Study finds reinforcement learning performance plateaus due to environmental interference.
problem Catastrophic interference hinders sample efficiency in reinforcement learning.
method Empirical study in ALE, controlled experiments, analysis of prediction errors.
result Interference causes performance plateaus and degrades policies used to reach them.
Study explores reinforcement learning in a complex game environment, analyzing rule inference and policy learning.
problem Learning optimal policies in environments with hidden rules.
method Investigated using the Game Of Hidden Rules (GOHR) environment, employing Feature-Centric and Object-Centric state representations with a Transformer-based A2C algorithm.
result Transformer-based A2C models outperform traditional methods in GOHR, demonstrating the effectiveness of representation strategies.
Bootstrapped DQN efficiently explores complex environments.
problem Efficient exploration in complex reinforcement learning environments.
method Randomized value functions and temporally-extended exploration.
result Exponentially faster learning and improved performance in Atari games.
This research develops secure DSVM algorithms using game theory.
problem Vulnerability of DSVM in adversarial environments.
method Game-theoretic framework to model conflicting interests between adversary and DSVM units.
result Guaranteed convergence of distributed learning algorithms without data or network topology assumptions.
The goal of machine learning is to provide solutions which are trained by data or by experience coming from the environment. Many training algorithms exist and some brilliant successes were achieved. But even in structured environments for machine learning (e.g. data mining or board games), most applications beyond the…
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.
Survey of challenges and future directions in applying RL to real-world settings.
problem Challenges in deploying RL in practical settings due to limited interaction and changing environments.
method Analysis of RL system design, implementation, and continual improvement.
result Need for theory and methodology to bridge research and application gap.
Improved online learning algorithms using ADP for adversarial environments.
problem Minimizing regret in adversarial online learning with vector-valued losses.
method Approximate dynamic programming to characterize lower Pareto frontier of expected losses.
result Improved performance bounds compared to existing online learning algorithms.
This paper proposes a self-supervised exploration method using disagreement of dynamics models.
problem Efficient exploration in stochastic environments with real robots.
method Train an ensemble of dynamics models and incentivize exploration to maximize disagreement.
result Sample-efficient exploration achieved without external rewards or reinforcement learning.
Procgen Benchmark uses procedurally generated games to test reinforcement learning.
problem Lack of diverse and high-quality training environments for reinforcement learning.
method Developed 16 procedurally generated game-like environments and used them to benchmark reinforcement learning.
result Procedurally generated environments are essential for training and evaluating reinforcement learning agents.
NetHack Learning Environment (NLE) tests RL algorithms, offering scalable, complex, and challenging gameplay.
problem Challenging environments for testing RL algorithms.
method Procedurally generated, stochastic, rich, and complex NetHack environment.
result Demonstrates empirical success for early stages of NetHack using RL.
New framework for multi-agent imitation learning in complex environments.
problem Multi-agent settings with multiple Nash equilibria and non-stationary environments.
method Generalized inverse reinforcement learning and actor-critic algorithm.
result Practical multi-agent imitation learning with good empirical performance.
Paper models human motivations in games using inverse RL.
problem Lack of understanding human motivations in game models.
method Multi-Motivation Behavior Modeling (MMBM) using inverse reinforcement learning.
result Reveals significant differences in value structures among player groups.
Project explores reinforcement learning solutions for sparse reward environments.
problem Difficulty in navigating environments with infrequent rewards.
method Contrast and investigate existing reinforcement learning solutions in various video games.
result Introduces a novel reinforcement learning solution combining curiosity and auxiliary tasks.
This chapter reviews recent advances in multi-agent reinforcement learning.
problem Theoretical foundations for multi-agent reinforcement learning are lacking.
method Selective overview of MARL algorithms with theoretical analysis.
result Identification of new research directions in MARL theory.
The paper evaluates various bonus-based exploration methods in the ALE and finds limited improvement in performance.
problem Improving exploration in reinforcement learning algorithms, especially in challenging games.
method Empirical evaluation of different reward bonuses on the Arcade Learning Environment.
result Recently developed bonus-based exploration methods do not significantly improve performance in challenging games.
A new reward learning module improves imitation learning in high-dimensional environments.
problem Challenges in high-dimensional environments for imitation learning.
method Generative model to generate intrinsic reward signals.
result Our method outperforms state-of-the-art IRL methods on Atari games.
Deep RL model learns 2.5D fighting games with height ambiguity.
problem Ambiguity in character height/depth and sequential action orders in 2.5D fighting games.
method Modified A3C network with Recurrent Info network for combo skill observation.
result Successfully learned and played Little Fighter 2 (LF2) 2.5D fighting game.