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Reinforcement Learning is a machine learning paradigm where agents learn to select optimal actions in sequential problems via rewards and penalties.
Reinforcement Learning (RL) is a subfield of machine learning where agents learn policies by trial-and-error and reward feedback to select actions. It models decision-making in sequential environments and suits control, optimization, and planning tasks. Use cases span robotics, game playing, and recommender or scheduling systems.
Total rewards summed over episodes to evaluate policy quality.
Number of training steps or interactions required to reach a target performance.
Stability of performance under changes in state or observation spaces.
Game agent that used RL combined with Monte-Carlo tree search to defeat human experts in Go.
Uses RL algorithms to optimize gaits and balance in simulated and real robots.
Use of RL to adapt NPC behavior and balancing in complex game environments.
Formulate the problem as an MDP or POMDP.
Design reward function and provide simulation environment.
Choose appropriate RL algorithm, train, evaluate and progressively transition to production.