Pong Game problem solving using RL - Policy Gradient with OpenAI Gym Framework and Tensorflow
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Updated
Mar 26, 2018 - Python
Pong Game problem solving using RL - Policy Gradient with OpenAI Gym Framework and Tensorflow
Work on SC2 minigames with reinforcement learning
Quickly learn policies for continuous control in sparse reward environments
Deep Q-Network, Actor-critic , Policy gradient implementation in python
Simple example of reinforcement learning using the "Policy Gradient" method to solve the optimized path of a maze.
Exploring different buffer sampling techniques to improve Hindisght Experience Replay on continuous control robotic application tasks. Continous action spaces & sparse rewards.
Multi-Agent DRL task analysed as a part of a course project for CS698R-21, IITK
Example A2C implementation with ReLAx
a collection of python notebooks using RL agents to play Atari games in OpenAI gym environments
Distributed RL platform with modified IMPALA architecture. Implements CLEAR, LASER V-trace modifications along with Attentive and Elite sampling experience replay methods.
My reports for the reinforcement learning class given at the ENS
Implementing Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning". using TensorFlow
policy gradient for pong
Evaluation and implementation of Reinforcement Learning algorithms. Python, OpenAI Gym, Tensorflow.
Implementations of Reinforcement Learning algorithms as baselines in Pytorch
[Reinforcement Learning, forked from Stable-baselines3] Étude des performances des algorithmes de Reinforcement Learning sur Pendulum
Programming Assignments for Reinforcement Learning Specialization
Scheduling TRPO's KL Divergence Constraint
OpenAI Gym game Lunar Lander tested with Deep Q Learning, Policy Gradient and Actor Critic algorithms.
Implement soft actor critic in pytorch to play a game of balancing pendulum in openai gym.
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