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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.

Implementation of RL Algorithms with PyTorch.

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

Solving the Atari Breakout environment using Stable Baselines

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

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.

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

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

Stable Baselines3