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Found 13 papers, 8 papers with code
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone.
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.
EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine
EnvPool is open-sourced at https://github. com/sail-sg/envpool.
Accelerated Policy Learning with Parallel Differentiable Simulation
In this work we present a high-performance differentiable simulator and a new policy learning algorithm (SHAC) that can effectively leverage simulation gradients, even in the presence of non-smoothness.
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions
We present an extended abstract for the previously published work TESSERACT [Mahajan et al., 2021], which proposes a novel solution for Reinforcement Learning (RL) in large, factored action spaces using tensor decompositions.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation
Operational Space Control (OSC) has been used as an effective task-space controller for manipulation.
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU.
Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger
We present a system for learning a challenging dexterous manipulation task involving moving a cube to an arbitrary 6-DoF pose with only 3-fingers trained with NVIDIA's IsaacGym simulator.
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning
Algorithms derived from Tesseract decompose the Q-tensor across agents and utilise low-rank tensor approximations to model agent interactions relevant to the task.
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?
Most recently developed approaches to cooperative multi-agent reinforcement learning in the \emph{centralized training with decentralized execution} setting involve estimating a centralized, joint value function.
GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning
Most Deep Reinforcement Learning (Deep RL) algorithms require a prohibitively large number of training samples for learning complex tasks.
Robotics
Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience
In doing so, we are able to change the distribution of simulations to improve the policy transfer by matching the policy behavior in simulation and the real world.
