DDPG

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17
Jun
Multi Agent Reinforcement Learning

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars I presented a session on Multi-Agent RL recently at the CellStrat AI Lab. Introduction :- In the normal Reinforcement Learning setup, you have one agent which interacts with the environment. It uses the Observation from the environment, performs actions and observes the rewards. In real life, many applications will involve several agents […]

Posted in: Reinforcement Learning,
Tags: Actor Critic, DDPG, Deep Deterministic Policy Gradients, MADDPG, Markov Games, Multi Agent Actor Critic, Multi Agent DDPG, Multi Agent RL, Policy Ensembles, policy gradients, Policy Inference, reinforcement learning, Unity ML,
20
Apr
DDPG and TD3

This post assumes that you have a strong understanding of the basics of Reinforcement Learning, MDP, DQN and Policy Gradient Algorithms. You can go through Policy Gradients to understand the derivation for Stochastic Policies In the previous post on Actor Critic, we saw the advantage of merging Value based and Policy based methods together. The […]

Posted in: Reinforcement Learning, Robotics,
Tags: Actor Critic, Actor Critic method, Bellman equation, DDPG, Deep Deterministic Policy Gradients, deterministic policy, Double DQN, DQN, Experience Replay, exploration vs exploitation, Fixed D Targets, policy gradients, Q learning, TD3 RL, Twin Delayed Double Deterministic Policy Gradients,
13
Apr
A Summary of Model-Free RL Algorithms

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #AlwaysUpskilling Reinforcement Learning (RL) refers to training agents with help of incentive-driven environments. RL typically involves a tuple of <state, action, reward> paradigm, which means that the agent has action choices to make in various states, and each action entails a potential reward. This also means that each state has a “value” […]

Posted in: Reinforcement Learning, Robotics,
Tags: Actor Critic, Actor Critic method, AI lab, cartpole, DDPG, deep Q learning, deep Q network, Deep Reinforcement Learning, deterministic policy, DQN, DRL course, gaming, markov decision process, markov process, Markov Reward Process, mdp, model-based RL, model-free RL, monte carlo, off-policy, on-policy, policy based methods, policy gradients, PPO, Q learning, Q table, Rainbow method, Reinforcement learning course, reward function, RL course, RL for gaming, RL training, SAC, stochastic policy, TD Learning, TD3, training in reinforcement learning, TRPO, Value-based methods,