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Found 8 papers, 6 papers with code
QuadSwarm: A Modular Multi-Quadrotor Simulator for Deep Reinforcement Learning with Direct Thrust Control
In addition to speed, such simulators need to model the physics of the robots and their interaction with the environment to a level acceptable for transferring policies learned in simulation to reality.
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning
Training generally capable agents that thoroughly explore their environment and learn new and diverse skills is a long-term goal of robot learning.
DexPBT: Scaling up Dexterous Manipulation for Hand-Arm Systems with Population Based Training
In this work, we propose algorithms and methods that enable learning dexterous object manipulation using simulated one- or two-armed robots equipped with multi-fingered hand end-effectors.
DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality
Our policies are trained to adapt to a wide range of conditions in simulation.
Megaverse: Simulating Embodied Agents at One Million Experiences per Second
We present Megaverse, a new 3D simulation platform for reinforcement learning and embodied AI research.
Agents that Listen: High-Throughput Reinforcement Learning with Multiple Sensory Systems
We are currently in the process of merging the augmented simulator with the main ViZDoom code repository.
Large Batch Simulation for Deep Reinforcement Learning
We accelerate deep reinforcement learning-based training in visually complex 3D environments by two orders of magnitude over prior work, realizing end-to-end training speeds of over 19, 000 frames of experience per second on a single GPU and up to 72, 000 frames per second on a single eight-GPU machine.
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
In this work we aim to solve this problem by optimizing the efficiency and resource utilization of reinforcement learning algorithms instead of relying on distributed computation.
