dennybritz

MDP

2023.01.21

Agent & Environment Interface: At each step t the agent receives a state S_t, performs an action A_t and receives a reward R_{t+1}. The action is chosen according to a policy function pi. The total return G_t is the sum of all rewards starting from time t . Future rewards are discounted at a discount rate gamma^k. Markov property: The environment's response at time t+1 depends only on the state ..

Reinforcement Learning

Introduction

2023.01.21

David Silver / UCL Course on RL https://www.davidsilver.uk/teaching/ Teaching - David Silver www.davidsilver.uk Reinforcement Learning (RL) is concerned with goal-directed learning and decision-making. In RL, an agent learns from experiences it gains by interacting with the environment. In Supervised Learning we cannot affect the environment. In RL, rewards are often delayed in time and the agen..

MDP

Introduction

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