openAI Gym

CellStrat > Research/Blog > openAI Gym
06
Feb
Policy Gradients – An Introduction

I conducted an Introductory session on Reinforcement Learning Policy Gradients (PG) at CellStrat AI Lab on 1st Feb 2020. The goal of this session was to explain the basic underlying principle of Policy Gradients. The session started off with a quick recap of Reinforcement Learning, so that the audience is well aware of the definitions […]

Posted in: Reinforcement Learning,
Tags: Acrobot, deep Q network, deepmind, DQN, experience replay buffer, fixed Q targets, gradient ascent, openAI Gym, policy based methods, policy gradients, Q learning, reinforcement learning, reward function, rewards based learning, value iteration algorithm,
29
Aug
Meeting Minutes from AI Lab Hands-On Workshop on Saturday 24th Aug in Bengaluru

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars Last Saturday, our team lead for Reinforcement Learning (RL) Shubha Manikarnike presented a fabulous hands-on workshop on RL and it’s various algorithms such as Markov Decision Process (MDP), Policy Gradients, Bellman equation, Q-learning etc. The session started with an Introduction to RL. There was a comparison on how this is different from […]

Posted in: Deep Learning, Reinforcement Learning,
Tags: Bellman equation, epsilon greedy technique, exploration vs exploitation, Frozen Lake, markov decision process, openAI Gym, Q learning, reinforcement learning, rewards based learning,