Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Gemini Adv | Llama 3:

Resume:

1.-Reinforcement learning (RL) is a general framework for building AI agents that can act in the world to achieve goals.

2.-RL agents take actions that influence the world, change the state, and affect future rewards the agent receives.

3.-The goal in RL is to select actions over time to maximize the sum of future rewards.

4.-Two main concepts in RL are policy (how the agent selects actions) and value (how good a state/action is).

5.-Policy-based RL directly searches for the optimal policy that achieves maximum reward from every state.

6.-Value-based RL estimates the optimal value function - the maximum reward achievable from each state by any policy.

7.-Examples of RL problems include robot control, user interaction optimization, games, and sequential decision making in machine learning.

8.-The RL problem is formalized as an agent interacting with an environment, receiving states, taking actions, and getting rewards.

9.-The optimal policy is one that maximizes future reward from every state. Finding this solves the RL problem.

10.-The optimal value function captures the maximum possible reward from each state. Finding this also solves the RL problem.

11.-Value iteration algorithms solve for the optimal value function by iteratively applying the Bellman optimality equation.

12.-Tabular value iteration methods don't scale to large state/action spaces. Neural networks can represent value functions to enable generalization.

13.-Q-learning trains a neural network to approximate the optimal action-value function by minimizing Bellman error.

14.-Naive Q-learning with neural networks is unstable due to correlated data, sensitivity to Q values, and varying reward scales.

15.-Deep Q-Networks (DQN) provide a stable solution using experience replay, target networks, and clipping rewards.

16.-Experience replay stores past transitions and samples from them randomly to break correlations and learn from varied past policies.

17.-Target networks are frozen for periods to keep Q-learning targets stable as the policy changes.

18.-DQN was applied to Atari games, learning to play from raw pixels using the same architecture and only game score.

19.-On many Atari games, DQN achieved human-level or superhuman performance after 2 weeks of training.

20.-Experience replay and target networks were both crucial for stabilizing learning and achieving good performance with DQN.

21.-Reward clipping in DQN was improved by a normalization technique to preserve reward scale while bounding gradients.

22.-Gorila architecture enables massively parallel DQN training by separating acting from learning and using distributed components.

23.-Gorila outperformed DQN on the majority of Atari games and reached DQN-level performance about 10x faster.

24.-Policy gradient methods directly optimize the policy to maximize rewards, useful for continuous action spaces.

25.-Deterministic policy gradient provides an end-to-end approach to adjust a policy network's parameters to improve expected reward.

26.-Actor-critic methods combine policy gradients with value estimation, using a critic to estimate Q-values and an actor to improve the policy.

27.-Continuous domain control from raw pixels was demonstrated using deterministic policy gradients with an actor-critic architecture.

28.-RL provides a general-purpose framework for AI. Many problems can be solved end-to-end by deep RL.

29.-Single deep RL agents can now solve a variety of challenging tasks specified as reward maximization problems.

30.-Limitations remain for complex problems with sparse rewards requiring long-term reasoning. Ongoing research aims to address these challenges.

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