a2c-algorithm

FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading

Implementing Deep Reinforcement Learning Algorithms in Python for use in the MuJoCo Physics Simulator

Accepted by AROB 2021. For letting agents in traffic simulation behave more like humans, we propose a unified mechanism for agents learn to decide various accelerations on deep reinforcement learning and generate a traffic flow behaving variously to simulate the real traffic flow.

Implementation of RL Algorithms with PyTorch.

Applying A2C-algorithm (Reinforcement Learning) for the control of a DC-motor

Implementing some RL algorithms (using PyTorch) on the CartPole environment by OpenAI.

A model describing how a car learns to control its acceleration by A2C_TD.

Solving the Atari Breakout environment using Stable Baselines

Using Imitation Learning for a Wordle agent

Implementation of the Advantage Actor-Critic (A2C) algorithm for training an agent to balance a pole in the CartPole environment using PyTorch and OpenAI Gym.

This repository explores Reinforcement Learning (RL) through hands-on implementations of key algorithms and environments. It demonstrates how agents learn by interacting with environments, optimizing rewards, and adapting to tasks ranging from Atari games to autonomous driving and custom simulations.

REINFORCE, Actor-Critic and A2C from scratch on CartPole-v1 against a DQN benchmark - the gap between AC at 370 and A2C at 497 comes down to one baseline subtraction, with AC's critic trained but never used in the actor's gradient

Personal sandbox project for testing reinforcement learning algorithms.

This repository displays the use of Reinforcement Learning, specifically QLearning, REINFORCE, and Actor Critic (A2C) methods to play CartPole-v0 of OpenAI Gym.

This project implements and evaluates various Reinforcement Learning (RL) and Evolutionary Algorithm (EA) agents designed to play the classic game of Tetris

Advantage Actor-Critic (A2C) reinforcement learning algorithm to detect emerging trends in tweets. The RL agent learns to optimize actions (post, edit, delete) based on engagement metrics such as likes, retweets, and quotes.

Using the "Advantage Actor Critic(A2C)" Reinforcement Learning method, the 'Agent' is trained to play Atari's Breakout.

Stable Baselines3

Hands-on implementations of Reinforcement Learning algorithms from scratch, progressing from classical methods to deep RL with practical experiments.

Custom implementations of RL algorithms that can solve complex tasks like Atari games