A model describing how a car learns to control its acceleration by A2C_TD.
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Updated
Jul 13, 2020 - GAML
A model describing how a car learns to control its acceleration by A2C_TD.
Implementing some RL algorithms (using PyTorch) on the CartPole environment by OpenAI.
This repository displays the use of Reinforcement Learning, specifically QLearning, REINFORCE, and Actor Critic (A2C) methods to play CartPole-v0 of OpenAI Gym.
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.
Implementing Deep Reinforcement Learning Algorithms in Python for use in the MuJoCo Physics Simulator
Using the "Advantage Actor Critic(A2C)" Reinforcement Learning method, the 'Agent' is trained to play Atari's Breakout.
Solving the Atari Breakout environment using Stable Baselines
Implementation of RL Algorithms with PyTorch.
Personal sandbox project for testing reinforcement learning algorithms.
Stable Baselines3
Applying A2C-algorithm (Reinforcement Learning) for the control of a DC-motor
For trading. Please star.
Custom implementations of RL algorithms that can solve complex tasks like Atari games
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