Ape-X is a distributed architecture aimed at deep reinforcement learning. It is designed to disconnect acting from learning by allowing different actors to interact with their own environment and accumulate experience in a shared memory. Ape-X uses prioritized experience replay to focus solely on the most useful data generated by actors, increasing efficiency and throughput while maintaining some latency. Lastly, the algorithm is off-policy, allowing it to combine data from different actors, bro
What is DD-PPO?
Decentralized Distributed Proximal Policy Optimization, commonly referred to as DD-PPO, is a method for distributed reinforcement learning in resource-intensive simulated environments. It is a policy gradient method for reinforcement learning that can be used with synchronous distribution. It is a distributed mechanism that has the potential to scale very well therefore making implementations very simple.
Proximal Policy Optimization (PPO)
Proximal Policy Optimization or PPO
What is IMPALA?
IMPALA, which stands for Importance Weighted Actor Learner Architecture, is an off-policy actor-critic framework. The framework separates acting from learning and allows learning from experience trajectories using V-trace. IMPALA is different from other agents like A3C because it communicates trajectories of experience to a centralized learner rather than gradients with respect to the parameters of the policy to a central parameter server. The decoupled architecture of IMPALA al
Introducing SEED RL: Revolutionizing Reinforcement Learning
SEED (Scalable, Efficient, Deep-RL) is a powerful reinforcement learning agent that is optimized for scalability, efficiency, and deep learning. It utilizes an innovative architecture that features centralized inference and an optimized communication layer. By harnessing two state-of-the-art distributed algorithms, IMPALA and V-trace (policy gradients), and R2D2 (Q-learning), SEED RL is at the forefront of advanced machine learning and
TorchBeast is an open-source platform that focuses on reinforcement learning research in PyTorch, a popular machine learning framework. It utilizes an implementation of the IMPALA algorithm that enables fast and asynchronous parallel training of RL agents.
What is Reinforcement Learning?
Reinforcement Learning, commonly abbreviated as RL, is a technique used in machine learning where an agent learns to interact with an environment by performing certain actions to get rewards. The goal of an R