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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.

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

Implementation of RL Algorithms with PyTorch.

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

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

Using Imitation Learning for a Wordle agent

Personal sandbox project for testing reinforcement learning algorithms.

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.

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

Stable Baselines3