Implementing Reinforcement Learning, namely Q-learning and Sarsa algorithms, for global path planning of mobile robot in unknown environment with obstacles. Comparison analysis of Q-learning and Sarsa
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Apr 25, 2022 - Python
Implementing Reinforcement Learning, namely Q-learning and Sarsa algorithms, for global path planning of mobile robot in unknown environment with obstacles. Comparison analysis of Q-learning and Sarsa
NLPGym - A toolkit to develop RL agents to solve NLP tasks.
Tensorflow 2 Reinforcement Learning Cookbook, published by Packt
Train a tic-tac-toe agent using reinforcement learning.
RL-Toolkit: A Research Framework for Robotics
Pytorch Implementation of RL algorithms
Flock and swarm multi-agent RL training environments implemented in JAX
Implementation of Continuous Control RL Algorithms
Mobile Apps (Android) as Environment for Reinforcement Learning Agents
Pytorch Implementation of RL algorithms
dITC through RL Code Foundation
Custom Reinforcement Learning Agents
This project focuses on comparing different Reinforcement Learning Algorithms, including monte-carlo, q-learning, lambda q-learning epsilon-greedy variations, etc.
A reinforcement learning agent navigating the OpenAI's FrozenLake environment
Our project focuses on the problem of generating synthetic levels of a game such that the levels can be used to learn an optimal policy for playing the game. Given a few pre-existing game levels we want to use deep generative models (like GANs) to generate new additional game levels. We will then train an RL agent on these levels to learn a gene…
Train SLM to use Tools with RL
A general-purpose remote environment for training RL agents.
Agentic AI involves several key components. This is an AI Agent for trading.These agents typically use reinforcement learning (RL) methods to optimize their behaviour over time through interactions with an environment.
This project trains and evaluates a Proximal Policy Optimization (PPO) agent to play the Atari game Atlantis using Stable Baselines3. The agent is trained with a Convolutional Neural Network (CNN) policy and evaluated for its performance in the game. It includes scripts for training, evaluating, and real-time gameplay rendering.
Collect more gift than an AI opponent in this fast-paced Christmas-themed game.
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