A2C, or Advantage Actor Critic, is a machine learning algorithm used for reinforcement learning tasks. It is a synchronous version of the A3C policy gradient method, and is becoming increasingly popular due to its efficient use of GPUs.
What is Reinforcement Learning?
Reinforcement learning is a type of machine learning that involves training an agent to make decisions based on trial and error, in order to maximize a reward signal. It is commonly used in areas such as robotics, game playing,
The Asynchronous Advantage Actor Critic (A3C) is a policy gradient algorithm used in reinforcement learning. This algorithm maintains a policy $\pi\left(a\_{t}\mid{s}\_{t}; \theta\right)$ and an estimate of the value function $V\left(s\_{t}; \theta\_{v}\right)$ in order to learn how to solve a given problem.
How A3C Works
A3C operates in the forward view and takes a mix of $n$-step returns to update both the policy and the value function. The policy and the value function are updated either a
An Overview of ACER: Actor Critic with Experience Replay
If you are interested in artificial intelligence and deep reinforcement learning, then you may have heard of ACER, which stands for Actor Critic with Experience Replay. This is a type of learning agent that uses experience replay, which essentially means it learns from past actions and choices to make better decisions in the future.
ACER can be thought of as an extension of another type of learning agent known as A3C. While A3C is an on-
What is ACKTR?
ACKTR stands for Actor Critic with Kronecker-factored Trust Region. It is a reinforcement learning method that helps machines learn from trial and error by rewarding or punishing them based on their actions.
How does ACKTR work?
ACKTR is an actor-critic method that optimizes both the actor and the critic using Kronecker-factored approximate curvature (K-FAC) with trust region.
In reinforcement learning, a machine learns by interacting with its environment. The machine receive
Ape-X DPG is a new method for efficiently training artificial intelligence agents in complex environments. This method combines two existing approaches, DDPG and prioritized experience replay, and utilizes the Ape-X architecture to improve performance.
What is DDPG?
DDPG stands for deep deterministic policy gradient. It is a type of algorithm used for training agents in reinforcement learning tasks, where an agent learns to take actions based on rewards received from the environment. DDPG is
What is DDPG?
Deep Deterministic Policy Gradient, commonly known as DDPG, is an algorithm used in the field of artificial intelligence that combines the actor-critic approach with insights from DQNs (Deep Q-Networks). DDPG is a model-free algorithm that is based on the deterministic policy gradient and can work efficiently over continuous action spaces.
How Does DDPG Work?
The DDPG algorithm makes use of the ideas from DQNs to minimize correlations between samples by training off-policy with
Overview of Deterministic Policy Gradient (DPG)
If you've ever seen a video game character improve its performance by learning from its environment, you have an idea of what reinforcement learning is. Reinforcement learning is a type of machine learning where an agent learns to make decisions based on its past experiences. A key aspect of reinforcement learning is the way the agent chooses its next action, or policy. DPG, or Deterministic Policy Gradient, is a policy gradient method for reinfor
Introduction to D4PG
D4PG, which stands for Distributed Distributional DDPG, is a machine learning algorithm that is used in reinforcement learning. This algorithm extends upon a similar algorithm called DDPG, which is short for Deep Deterministic Policy Gradient. The idea behind D4PG is to make improvements to DDPG so that it can perform better on harder problems. One of the ways that D4PG improves upon DDPG is by using something called distributional updates. Another way that D4PG improves up
Fisher-BRC is an algorithm used for offline reinforcement learning. It is based on actor-critic methods that encourage the learned policy to stay close to the data. The algorithm uses a neural network to learn the state-action value offset term, which can help regularize the policy changes.
Actor-critic algorithm
The actor-critic algorithm is a combination of two models - an actor and a critic. The actor is responsible for taking actions in the environment, and the critic is responsible for e
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
Introduction to MADDPG
MADDPG stands for Multi-agent Deep Deterministic Policy Gradient. It is a type of algorithm that allows multiple agents to learn and cooperate with one another based on their collective observations and actions. This algorithm is an extension of the DDPG algorithm, which stands for Deep Deterministic Policy Gradient.
What is DDPG?
DDPG is an algorithm used for reinforcement learning. It involves approximating the optimal state-value function and the optimal policy for
Overview of MDPO: A Trust-Region Method for Reinforcement Learning
If you are interested in reinforcement learning, you have probably heard about the Mirror Descent Policy Optimization (MDPO) algorithm. MDPO is a policy gradient algorithm based on the trust-region method that iteratively solves a problem that minimizes a sum of two terms: a linearization of the standard reinforcement learning objective function and a proximity function that restricts two consecutive updates to be close to each
Introducing myGym: A Tool for Fast Prototyping of Neural Networks in Robotic Manipulation and Navigation
myGym is a toolkit designed to aid in the development and rapid prototyping of neural networks in the field of robotic manipulation and navigation. The modular design of the toolkit means that it can be adapted to different robots, environments, and tasks, making it a versatile tool for machine learning researchers.
Features of myGym
The features of myGym include pre-trained neural networ
NoisyNet-A3C is an improved version of the well-known A3C method of neural network training. It employs noisy linear layers to replace the traditional epsilon-greedy exploration method in the original deep Q-network (DQN) model.
What is A3C?
As mentioned earlier, NoisyNet-A3C is a modification of A3C. Therefore, it would be useful to know the basic principles behind A3C before delving into NoisyNet-A3C.
A3C stands for Asynchronous Advantage Actor-Critic. It is a method used to train neural n
Overview of Proximal Policy Optimization (PPO)
Proximal Policy Optimization (PPO) is a form of policy gradient method for reinforcement learning. PPO was created to provide an algorithm that combines efficient data usage and reliable performance, while using only first-order optimization. PPO involves modifying the objective to penalize changes that move away from the probability ratio of one, which provides an upper bound on the unclipped objective. In this article, we will explain PPO in more
Overview of REINFORCE Algorithm in Reinforcement Learning
Reinforcement learning is a type of machine learning where agents learn how to interact with an environment through trial and error. The goal is for the agent to learn how to take actions that maximize a reward signal. This type of learning is commonly used in robotics, gaming, and other industries. One of the most popular algorithms used in reinforcement learning is the REINFORCE algorithm.
What is the REINFORCE Algorithm?
The REINFO
Introduction to Robust Predictable Control
Robust Predictable Control, or RPC, is an advanced algorithm that allows machines to learn how to make decisions based on only a small amount of information. RPC combines different methods from machine learning to create a powerful system for making accurate predictions about the future. By accurately predicting what will happen next, the system can avoid spending unnecessary time observing new information, thus improving the efficiency of decision-mak
Soft Actor-Critic (Autotuned Temperature): An Overview
Reinforcement learning is a type of machine learning that involves training an agent to take actions based on the environment it is in. Soft Actor-Critic (SAC) is a popular reinforcement learning algorithm that has been modified with Autotuned Temperature to improve its performance.
SAC is used to find the maximum entropy policy, which means choosing actions that have the highest probability of reaching a particular goal while also account