Model-Free Reinforcement Learning Algorithm

Context:
- It can be characterized by its ability to learn from the environment's responses to its actions, rather than from a predetermined model.
- It can be applied in various scenarios, including game playing, autonomous navigation, and resource management, where the environment dynamics are not fully known.
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Example(s):
Counter-Example(s):
- Model-Based (Reinforcement Learning).
See: Markov Decision Process, Trial And Error.

References

(Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/Model-free_(reinforcement_learning) Retrieved:2020-12-10.
- In reinforcement learning (RL), a model-free algorithm (as opposed to a model-based one) is an algorithm which does not use the transition probability distribution (and the reward function) associated with the Markov decision process (MDP) , which, in RL, represents the problem to be solved. The transition probability distribution (or transition model) and the reward function are often collectively called the "model" of the environment (or MDP), hence the name "model-free". A model-free RL algorithm can be thought of as an "explicit" trial-and-error algorithm . An example of a model-free algorithm is Q-learning.

Algorithm	Description	Model	Policy	Action Space	State Space	Operator
DQN	Deep Q Network	Model-Free	Off-policy	Discrete	Continuous	Q-value
DDPG	Deep Deterministic Policy Gradient	Model-Free	Off-policy	Continuous	Continuous	Q-value
A3C	Asynchronous Advantage Actor-Critic Algorithm	Model-Free	On-policy	Continuous	Continuous	Advantage
TRPO	Trust Region Policy Optimization	Model-Free	On-policy	Continuous	Continuous	Advantage
PPO	Proximal Policy Optimization	Model-Free	On-policy	Continuous	Continuous	Advantage
TD3	Twin Delayed Deep Deterministic Policy Gradient	Model-Free	Off-policy	Continuous	Continuous	Q-value
SAC	Soft Actor-Critic	Model-Free	Off-policy	Continuous	Continuous	Advantage