Knowledge Vault 2/50 - ICLR 2014-2023
Maruan Al-Shedivat Trapit Bansal Yuri Burda Ilya Sutskever Igor Mordatch Pieter Abbeel ICLR 2018 - Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments
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Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Gemini Adv | Llama 3:

graph LR classDef meta fill:#f9d4d4, font-weight:bold, font-size:14px; classDef nonstationarity fill:#d4f9d4, font-weight:bold, font-size:14px; classDef robosumo fill:#d4d4f9, font-weight:bold, font-size:14px; classDef adaptation fill:#f9f9d4, font-weight:bold, font-size:14px; classDef limitations fill:#f9d4f9, font-weight:bold, font-size:14px; A[Maruan Al-Shedivat et al
ICLR 2018] --> B[Gradient-based meta-learning algorithm. 1] A --> C[Learn, adapt from limited experience. 2] A --> D[Nonstationary environments: complexity,
changing dynamics. 3] D --> E[Classical approaches impractical
with modern RL. 4] A --> F[Nonstationarity as multi-task learning. 5] B --> G[Anticipate changes, update policies. 6] A --> H[Multi-agent: challenging, emergent complexity. 7] A --> I[RoboSumo: competitive 3D environment. 8] I --> J[Iterated adaptation games. 9] I --> K[Nonstationary, adversarial, natural curriculum. 10] B --> L[Efficient adaptation in few-shot. 11] I --> M[Meta-learners fittest in population. 12] D --> N[Limited interaction before each change. 13] B --> O[Nonstationarity as stationary task sequence. 14] H --> P[Agents simultaneously learning, changing. 15] I --> Q[Successive rounds, adapting strategies. 16] B --> R[Compared to baselines. 17] D --> S[Outperforms on nonstationary locomotion. 18] I --> T[Adapts to increasingly skilled opponent. 19] B --> U[Experience needed for adaptation methods. 20] I --> V[Meta-learners ranked highest in
iterated competitions. 21] I --> W[Meta-learners dominated over generations. 22] B --> X[Optimized via backpropagation. 23] B --> Y[Importance weight correction for
off-policy data. 24] B --> Z[Diverse tasks enable
generalizable rules. 25] B --> AA[Assumptions, computational expense, instability. 26] B --> AB[Consistent task structure, interaction
for adaptation data. 27] B --> AC[Modifiable update rule for
different task structures. 28] B --> AD[Principled optimization of adaptation. 29] class A,B,F,O,X,Y,Z,AA,AB,AC,AD meta; class C,D,E,N nonstationarity; class G,L,R,S,U adaptation; class H,I,J,K,M,P,Q,T,V,W robosumo; class AA limitations;

Resume:

1.-The paper presents a gradient-based meta-learning algorithm for continuous adaptation in nonstationary and competitive environments.

2.-The ability to continuously learn and adapt from limited experience in nonstationary environments is seen as a milestone towards general intelligence.

3.-Real-world environments are often nonstationary due to complexity, changing dynamics or objectives over time, or the presence of other learning agents.

4.-Classical approaches to nonstationarity like context detection and tracking become impractical with modern deep RL methods that require many samples.

5.-The paper proposes tackling nonstationary environments as a multi-task learning problem using learning-to-learn or meta-learning approaches.

6.-The gradient-based meta-learning algorithm enables RL agents to learn to anticipate changes in the environment and update their policies accordingly.

7.-Multi-agent environments are particularly challenging and interesting due to the emergent complexity arising from agents learning and changing concurrently.

8.-The paper introduces RoboSumo, a new 3D physics-based environment where robot agents can compete against each other.

9.-Iterated adaptation games are proposed for testing continuous adaptation - agents repeatedly compete while being allowed to update their policies between rounds.

10.-The competitive setting makes the environment both nonstationary and adversarial, providing a natural curriculum and encouraging robust strategies.

11.-Meta-learning enables significantly more efficient adaptation than reactive baselines in the few-shot regime in both single- and multi-agent settings.

12.-Experiments suggest that agents using meta-learned adaptation strategies tend to be the fittest in a population that learns and competes.

13.-Learning under nonstationary conditions is challenging as changes in the environment allow only limited interaction before each change.

14.-The meta-learning approach casts nonstationarity as a sequence of stationary tasks and optimizes for a rule to update policies as tasks change.

15.-While many aspects like agent physiology can induce nonstationarity, environments with multiple learning agents are especially challenging and interesting.

16.-From an individual agent's perspective, multi-agent environments are nonstationary as other agents are simultaneously learning and changing their behaviors.

17.-RoboSumo allows iterated games where a pair of agents compete in successive rounds while adapting to each other's changing strategies.

18.-The meta-learning agents are compared to baselines including no adaptation, implicit adaptation via RL2, and adaptation via tracking.

19.-On nonstationary locomotion tasks, the meta-learned policies outperform other methods in terms of continuous improvement over successive environment changes.

20.-In RoboSumo, the meta-learned strategies show superior performance than baselines when adapting to an opponent that gets increasingly skilled over rounds.

21.-Experiments illuminate how much experience is needed for different adaptation methods to successfully adapt to the changes.

22.-With a population of diverse agents, those using meta-learned strategies ranked the highest based on TrueSkill scores from iterated competitions.

23.-Over successive generations of evolution of the population based on adaptation performance, meta-learners came to dominate the pool.

24.-Meta-learning rules are optimized at training time via backpropagation through the policy update steps to maximize performance after adaptation.

25.-Importance weight correction is used to make the meta-update unbiased when adapting at execution time with off-policy data.

26.-Simultaneous training on diverse tasks (nonstationary locomotion scenarios or different opponents) allows meta-learning of generalizable adaptation rules.

27.-Limitations include sensitive assumptions about task structure, computational expense of higher-order gradients, and potential instability under drastic shifts between tasks.

28.-Key aspects enabling this approach are consistent task structure between training and testing, and ability to interact to gather data for adaptation.

29.-The framework is general in that different notions of task structure can be considered by modifying the meta-learning update rule.

30.-The approach provides a principled framework for optimizing adaptation, offering a path to creating more flexible and robust learners.

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