Reinforcement learning course
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,