ARC-RL is a new suite of four MuJoCo continuous-control environments featuring game-inspired hexapod and quadruped morphologies, a single closed-form multi-component reward function, CPG demonstrators, and empirical comparisons of online and offline-to-online RL algorithms.
Unity: A general platform for intelligent agents
7 Pith papers cite this work. Polarity classification is still indexing.
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Infernux is a game engine that uses batch data bridging and Numba JIT to make Python scripting performant within a Vulkan C++ core.
REAP trains an end-to-end SAC policy with behavior cloning and collision penalties inside a 3DGS Real2Sim simulator and transfers it to physical vehicles, succeeding in narrow mechanical parking slots.
A C++ Dec-POMDP simulator using data-oriented design and zero-copy PyTorch integration achieves up to 33 million steps per second on a 16-core CPU, enabling multi-agent policy training in minutes with PPO, DQN, and SAC.
An empirical literature analysis reveals a bifurcation in RL environments into Semantic Prior (LLM-dominated) and Domain-Specific Generalization ecosystems with distinct cognitive fingerprints.
ORRB is an open-source remote rendering backend that pairs Unity3d with MuJoCo for high-throughput, customizable visual domain randomization in robotics environments.
The paper delivers a concise, self-contained tutorial on foundational DRL algorithms including REINFORCE and PPO and DIL methods including behavioral cloning, DAgger, and GAIL for embodied agents.
citing papers explorer
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ARC-RL: A Reinforcement Learning Playground Inspired by ARC Raiders
ARC-RL is a new suite of four MuJoCo continuous-control environments featuring game-inspired hexapod and quadruped morphologies, a single closed-form multi-component reward function, CPG demonstrators, and empirical comparisons of online and offline-to-online RL algorithms.
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Infernux: A Python-Native Game Engine with JIT-Accelerated Scripting
Infernux is a game engine that uses batch data bridging and Numba JIT to make Python scripting performant within a Vulkan C++ core.
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REAP: Reinforcement-Learning End-to-End Autonomous Parking with Gaussian Splatting Simulator for Real2Sim2Real Transfer
REAP trains an end-to-end SAC policy with behavior cloning and collision penalties inside a 3DGS Real2Sim simulator and transfers it to physical vehicles, succeeding in narrow mechanical parking slots.
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A High-Throughput Compute-Efficient POMDP Hide-And-Seek-Engine (HASE) for Multi-Agent Operations
A C++ Dec-POMDP simulator using data-oriented design and zero-copy PyTorch integration achieves up to 33 million steps per second on a 16-core CPU, enabling multi-agent policy training in minutes with PPO, DQN, and SAC.
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From Pixels to Digital Agents: An Empirical Study on the Taxonomy and Technological Trends of Reinforcement Learning Environments
An empirical literature analysis reveals a bifurcation in RL environments into Semantic Prior (LLM-dominated) and Domain-Specific Generalization ecosystems with distinct cognitive fingerprints.
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ORRB -- OpenAI Remote Rendering Backend
ORRB is an open-source remote rendering backend that pairs Unity3d with MuJoCo for high-throughput, customizable visual domain randomization in robotics environments.
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An Introduction to Deep Reinforcement and Imitation Learning
The paper delivers a concise, self-contained tutorial on foundational DRL algorithms including REINFORCE and PPO and DIL methods including behavioral cloning, DAgger, and GAIL for embodied agents.