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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.
Using the "Advantage Actor Critic(A2C)" Reinforcement Learning method, the 'Agent' is trained to play Atari's Breakout.
Personal sandbox project for testing reinforcement learning algorithms.
Solving the Atari Breakout environment using Stable Baselines
Applying A2C-algorithm (Reinforcement Learning) for the control of a DC-motor
Implementing some RL algorithms (using PyTorch) on the CartPole environment by OpenAI.
Stable Baselines3
This repository displays the use of Reinforcement Learning, specifically QLearning, REINFORCE, and Actor Critic (A2C) methods to play CartPole-v0 of OpenAI Gym.