gaming
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” […]
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,
09
Feb
Use Cases of AI by Percentage of Resources Spent
Tags:
AI,
artificial intelligence,
Automation,
Automotive,
cyber-intelligence,
DeepLearning,
digital marketing,
e-mail,
ediscovery,
elearning,
emarketing,
gaming,
Healthcare,
industrial automation,
internet,
IoT,
language translation,
Legal,
Machinelearning,
manufacturing,
marketing,
marketing automation,
ML,
peronalisation,
predictive maintenance,
research,
Retail,
Risk Analysis,
robotics,
search,
segmentation,
semiconductors,
sensors,
supplychain,
Telco,
Telecom,
telematics,
24
Jan
Predicted Artificial Intelligence Revenue Share by Industry Globally in 2025
Tags:
advertising,
Aerospace,
agriculture,
AI,
artificialintelligence,
Automation,
Automotive,
bots,
Consumer,
DeepLearning,
Defense,
Eucation,
Fashion,
Finance,
gaming,
Gas & Mining,
government,
Healthcare,
Investment,
IT,
Legal,
Life Sciences,
Machinelearning,
manufacturing,
Media & Entertainment,
ML,
ogistics,
Oil,
Real Estate,
Retail,
Sports,
Telecom,
transport,
22
Jan
19
Jan