This sampling process is embodied as the policyπ, which is responsible for outputting an actiona conditioned on past environmental states{s}.

Consider An n= grad (r)+ A grad (r)+ C| 0 B @ 01 10 ... 1 C

Yetinreinforcementlearning,duetothenatureofthe Bellman equation, there isanopportunity toalsoexploit temporal regularization based on smoothness in value estimates over trajectories. This paper explores a class of methods for temporal regularization.

Neural Information Processing Systems http://nips.cc/

Then, at policy fork steps.

This creates a tight feedback loop between representation learning,memory,reinforcementlearning,andsampling-basedplanning.

The history of learning for control has been an exciting back and forth between twobroad classes ofalgorithms: planning andreinforcement learning.

While it is well suited to describe turn-based decision problems such as board games, this framework is ill suited for real-time applications in which the environment's state continues to evolve while the agent selects an action (Travnik et al., 2018). Nevertheless, this framework hasbeen used forreal-time problems using what areessentially tricks, e.g.

To this end, Andrychowicz et al.[1] introduced Hindsight Experience Replay (HER), which can rapidly train goal-conditioned policies by retroactively imagining failed trajectories as successful ones.

RL agents learn policies from experience.