Core Tenants Impact on RL Algorithm Selection

Question 1

Number of environment interactions needed for good policy. Critical for costly real-world data

Answer 1

Sample Efficiency

Trade-off with wall clock time: cheap samples favor model-free; expensive samples favor model-based.

Question 2

In RL what is Stability & Convergence related to?

Answer 2

Ease and speed of convergence; sensitivity to seeds/hyperparameters.

Improvements often add hyperparameters, hurting generalization. Local optima common.

Question 3

Performance on unseen tasks/situations.

Answer 3

Generalization

Trade-off with model fidelity: explicit models are efficient but brittle; direct policies are adaptable but less efficient.

Question 4

Underlying constraints (e.g., continuity, observability, action space type).

Answer 4

Assumptions & Approximations

Violating assumptions leads to failure. Problem structure dictates suitable algorithms

Question 5

Ease of computation and ability to scale horizontally.

Answer 5

Computational Simplicity & Parallelism

High parallelism can reduce wall clock time even with low sample efficiency.

Question 6

Effectiveness in discovering states/actions.

Answer 6

Exploration

Violating assumptions leads to failure. Problem structure dictates suitable algorithms.

Question 7

Error from assumptions vs. sensitivity to data fluctuations.

Answer 7

Bias vs. Variance

Reducing variance often hurts sample efficiency; conflicting objectives.

Question 8

Bias vs. Variance
Identify the matching model with these descriptions -

a. High variance, zero bias example
b. Low variance, biased example
c. Lower variance, but hyperparameter sensitive.
d. Model example that is vulnerable to variance

(1. one step Temporal Difference,
2. Monte-Carlo,
3. Policy Gradient,
4. off-policy Temporal Difference(TD) )

Answer 8

Bias vs. Variance

1. one step Temporal Difference(TD)
   b. Low variance, biased example
2. Monte-Carlo
   a. High variance, zero bias example
3. Policy Gradient
   d. Model example that is vulnerable to variance
4. Off-policy Temporal Difference(TD)
   c. Lower variance, but hyperparameter sensitive.

Question 9

Computational Simplicity & Parallelism
rate the models computation cost & Parallelism
Model-based
Computational cost - (Low, Medium, High)

Value-based/Model-based
Parallelizability - (Easy or Hard)

Evolutionary
Computational cost - (Low, Medium, High)
Parallelizability——— (Low or high)

Model-free:
Computational cost?

Answer 9

Computational Simplicity & Parallelism

Model-based
Computational cost - High( iterative methods)

Value-based/Model-based
Parallelizability - Hard

Evolutionary
Computational cost - Low
Parallelizability - high

Model-free:
Computational cost - Appealing when samples are cheap.

Question 10

How does Model-based methods do with Sample efficiency?

Answer 10

Model-based methods generally stand out as the most sample-efficient because they leverage an internal understanding of the system dynamics, which significantly reduces the volume of samples needed for learning

Question 11

On-policy vs. Off-policy Learning

Answer 11

The fundamental distinction between on-policy and off-policy learning lies in the relationship between the policy used to collect data and the policy being optimized.

Question 12

On-policy methods

operate with a ___ that serves both as the behavior policy for sampling data and the target policy for optimization

Answer 12

1.single policy

operate with a single policy that serves both as the behavior policy for sampling data and the target policy for optimization

Question 13

Policy Gradient:
On-policy or Off-policy Learning

Answer 13

On-policy

Policy Gradient methods exemplify this, single policy usage where actions are sampled from a policy (π), and the observed rewards are then used to optimize that same policy

Question 14

are **Value-Learning ** methods
On-policy or Off-policy Learning?

Answer 14

it depends.

some value-learning methods, observed rewards are used to fit a Q-value function, which subsequently derives the exact same policy used for data collection

Question 15

How does On-policy methods
do with sample effiecency?

Answer 15

A critical implication of this single-policy approach is that any change in the policy during optimization typically necessitates the collection of new samples, as old samples become irrelevant for the updated policy

This often leads to poor sample efficiency, as data collected for one gradient update may be inefficient for subsequent updates

Question 16

How does on-policy methods do with
Converage? Easy or hard

Answer 16

on-policy methods are sometimes claimed to converge faster compared to off-policy approaches

Question 17

How to improve
on-policy methods
Exploration performance?

Answer 17

To enhance exploration, on-policy methods may introduce “hacks” like ε-greedy policies, which are inherently sub-optimal for the ultimate target policy

Question 18

The distinction between Model-free vs. Model-based Reinforcement Learning is?

Answer 18

The distinction between model-free and model-based Reinforcement Learning centers on whether the algorithm explicitly learns or utilizes a model of the environment’s dynamics.

Question 19

Describe Model-free RL

Answer 19

Model-free RL approaches focus on directly learning a policy or value functions from observed samples, without constructing or relying on an explicit model of the environment’s dynamics.1

Question 20

The choice between model-free and model-based approaches can often be framed by a

Answer 20

a “policy-centric vs. model-centric” perspective: which is conceptually simpler to model for a given task?1

Is it easier to define what the agent should do (policy) or how the environment behaves (model)?

.

Question 21

a “policy-centric vs. model-centric” perspective: which is conceptually simpler to model for a given task?1
(example)

Answer 21

For instance, in the cart-pole problem, balancing the pole might be more intuitively modeled by a policy (e.g., “move left if the pole falls left”) without needing to understand the underlying physics. In contrast, for a game like Go, where the rules (the model) are clearly defined, a model-based search for promising moves might be more straightforward than directly learning a policy for a beginner

Question 22

Value-Learning vs. Policy Gradient Methods

Answer 22

Value-learning and Policy Gradient methods represent two fundamental approaches to optimizing an agent’s behavior in Reinforcement Learning, differing primarily in what they directly optimize.

Question 23

Value-learning methods, such as Q-learning and DQN, aim

Answer 23

Value-learning methods, such as Q-learning and DQN, aim to estimate the optimal value function (e.g., the Q-value function), which then implicitly defines the optimal policy. The policy is derived by selecting the action that maximizes the estimated Q-value for a given state

Question 24

Policy Gradient (PG) methods ( by contrast to Value learning)

Answer 24

Policy Gradient (PG) methods, by contrast, directly optimize the policy function itself. They achieve this by performing gradient ascent on an objective function that quantifies the expected return, effectively making high-reward actions more probable

Question 25

The core tension between Value-Learning and Policy Gradient methods

Answer 25

lies in their approach to optimization, representing an efficiency versus directness dilemma. Value-Learning prioritizes sample efficiency and stability (via off-policy and experience replay) at the cost of direct policy control and interpretability, particularly in continuous action spaces

Question 26

On-policy ---------------- Core Characteristic/Mechanism

Answer 26

Single policy for sampling and optimization.

Question 27

On-policy ------------------- Strengths

Answer 27

Simpler, less variance, potentially faster convergence (per step).

Question 28

On-policy ------------------- Weaknesses

Answer 28

Poor sample efficiency (needs new samples for policy changes), exploration often relies on "hacks."

Question 29

On-policy ------------------- Representative Algorithms

Answer 29

SARSA, Vanilla Policy Gradient, A3C, TRPO, PPO

Question 30

Understand why Actor-Critic methods were developed as a hybrid approach, combining the strengths of 1 and 2 reinforcement learning

Answer 30

1. policy-based(Actor) 2. value-based(Critic)

Question 31

Off-policy ---------------- Core Characteristic/Mechanism

Answer 31

Separate behavior and target policies.

Question 32

Off-policy ------------------- Strengths

Answer 32

Improved sample efficiency (reuses samples via importance sampling/replay buffer), better exploration control, enhanced stability (e.g., DQN).

Question 33

Off-policy ------------------- Weaknesses

Answer 33

Can be more complex, hyperparameter sensitive, may have slower convergence in some cases.

Question 34

Off-policy ------------------- Representative Algorithms

Answer 34

Q-learning, DQN, DDPG

Question 35

Model-free RL ---------------- Core Characteristic/Mechanism

Answer 35

Learns policy/value functions directly from samples, ignores dynamics.

Question 36

Model-free RL ------------------- Strengths

Answer 36

Fewer assumptions, wider applicability, good at complex policies, better generalization in some tasks.

Question 37

Model-free RL ------------------- Weaknesses

Answer 37

Less sample efficient, vulnerable to overfitting with complex models.

Question 38

Model-free RL ------------------- Representative Algorithms

Answer 38

Q-learning, DQN, Policy Gradient methods (e.g., PPO, A3C)

Question 39

In reinforcement learning, what is a policy?

Answer 39

In reinforcement learning, a policy defines an agent's strategy for interacting with an environment. It essentially maps the agent's current state to an action, guiding its behavior and determining how it should behave in various situations. The goal is to learn an optimal policy that maximizes the cumulative reward over time.

Question 40

Model-based RL ---------------- Core Characteristic/Mechanism

Answer 40

Learns/uses environment model to derive optimal policy.

Question 41

Model-based RL ------------------- Strengths

Answer 41

Highly sample efficient, scalable (self-trained).

Question 42

Model-based RL ------------------- Weaknesses

Answer 42

More assumptions, less generalized solutions, model can be complex to train, doesn't optimize policy directly.

Question 43

Model-based RL ------------------- Representative Algorithms

Answer 43

PILCO, Guided Policy Search (GPS)

Question 44

Value-learning ---------------- Core Characteristic/Mechanism

Answer 44

Estimates optimal value function (e.g., Q-value) to derive implicit policy.

Question 45

Value-learning ------------------- Strengths

Answer 45

More sample efficient (especially with off-policy/replay), can improve stability.

Question 46

Value-learning ------------------- Weaknesses

Answer 46

Difficult for continuous control, less interpretable, no convergence guarantee with deep nets, hyperparameter sensitive, prone to instabilities.

Question 47

Value-learning ------------------- Representative Algorithms

Answer 47

Q-learning, DQN

Question 48

Policy Gradient ---------------- Core Characteristic/Mechanism

Answer 48

Directly optimizes policy function via gradient ascent.

Question 49

Policy Gradient ---------------- Core Characteristic/Mechanism

Answer 49

Easy to set up, interpretable policy, supports discrete and continuous control, converges for convex functions.

Question 50

Policy Gradient ---------------- Core Characteristic/Mechanism

Answer 50

High variance, poor sample efficiency, challenging learning rate tuning, can fall into local optima.

Question 51

Value-learning ---------------- Core Characteristic/Mechanism

Answer 51

REINFORCE, TRPO, PPO, A2C/A3C (Actor-Critic)

Question 52

which methods exhibit high variance but possess zero bias?

Answer 52

Monte-Carlo (MC) methods This characteristic arises because a stochastic policy generates different trajectories across different runs, and even minute changes in a trajectory can lead to significantly different cumulative rewards, resulting in high variance over multiple runs.

Question 53

which method employs a single-step lookup in computing the value function, which results in low variance because only one action is involved and the change is minimal?

Answer 53

1-step Temporal Difference (TD) learning, the outcome is biased, particularly during the initial phases of training

Question 54

while Monte-carlo methods exhibit high variance with zero bias and 1-step Temporal Difference learning has low varance. which other method is highly susceptible to variance, which can severely impede model convergence?

Answer 54

Policy Gradient (PG) methods are highly susceptible to variance, which can severely impede model convergence

Question 55

what Strategies are use to mitigate variance in PG?

Answer 55

utilizing a more effective advantage function or imposing restrictions on gradient changes through trust regions. Increasing the number of samples—for instance, by running MC simulations many times or employing a large batch size—can also reduce variance, but this approach simultaneously negatively impacts sample efficiency

Question 56

which methods adapt TD concepts, thereby achieving lower variance?

Answer 56

Off-policy actor-critic, Q-learning, and other value-fitting methods Nevertheless, these methods frequently demand extensive hyperparameter searches to function correctly, and this hypersensitivity to hyperparameter tuning can detrimentally affect both stability and generalization

Question 57

The explicit statement that increasing sampling to reduce variance hurts what?

Answer 57

sample efficiency revealing a crucial interconnectedness between fundamental trade-offs

Question 58

It is not merely about balancing bias and variance in isolation, but also understanding how this balance impacts the cost of learning, which is frequently measured by

Answer 58

sample efficiency.

Question 59

Researchers and practitioners must consciously decide which form of "cost"—bias, variance, or data—they are most willing to incur. Is related to what fundamental principle in ML development?

Answer 59

the "no free lunch" principle applying not just to algorithm selection but also to the internal design choices within an algorithm.

Question 60

How is Sample Efficiency and Wall Clock Time related?

Answer 60

The relationship between sample efficiency and wall clock time is a critical trade-off that influences practical RL deployment. Sample Efficiency vs Wall Clock Time

Question 61

If an algorithm operates with fewer samples, it inherently requires

Answer 61

more effective optimization per sample. Sample efficiency, while often a primary concern, is not necessarily the ultimate goal

Question 62

Which methods are the most sample-efficient; however, they involve the most complex optimizations calculations per sample?

Answer 62

Model-based methods

Question 63

Which methods may achieve shorter wall clock times when data sampling is inexpensive or can be performed efficiently through simulation?

Answer 63

Model-free methods

Question 64

For tasks such as robotic control, where collecting physical samples is prohibitively expensive, (model-based or model-free) methods are often favored.

Answer 64

model-based methods are often favored. In these scenarios, the time spent on trajectory planning, which is part of the model-based approach, constitutes a relatively small fraction of the overall wall clock time

Question 65

which methods, despite their low sample efficiency, can be practical solutions due to their inherently low computational complexity and high potential for parallelism, which significantly reduces overall wall clock time?

Answer 65

Evolutionary methods,

Question 66

The viability of _______________ increases with the amount of samples available, which in turn influences which methods prove more efficient in terms of wall clock time.

Answer 66

data parallelism

Question 67

The trade-off between sample efficiency and wall clock time introduces an economic dimension to algorithm selection. The "cost" of _____________________ —whether from real-world interactions or simulations—and the "cost" of _______________—whether sequential or parallel become primary drivers.

Answer 67

first blank -- "cost" of data acquisition second blank -- "cost" of computation

Question 68

The observation that _______________ is not always the ultimate goal and that better optimization per sample is required when fewer samples are used, alongside the specific examples of model-based methods being computationally complex despite high sample efficiency, and model-free methods being faster with cheap samples, illustrates this economic dimension. The _______________ is the ultimate practical metric, representing a product of sample efficiency, computational efficiency per sample, and parallelizability.

Answer 68

first blank -- sample efficiency second blank -- "wall clock time"

Question 69

what is the ultimate practical metric, representing a product of sample efficiency, computational efficiency per sample, and parallelizability?

Answer 69

"wall clock time"

Question 70

If samples are expensive, minimizing ___________ becomes paramount. If samples are cheap, minimizing __________________becomes the priority.

Answer 70

first blank -- their number second blank -- the computational time to process them, through simpler calculations or parallelism,

Question 71

A company with access to vast simulation infrastructure might prefer a ______________ algorithm, while a robotics lab might prioritize ____________ due to the prohibitive cost of physical interactions. This underscores that RL algorithm selection is a strategic decision involving resource allocation

Answer 71

first blank -- model-free, parallelizable second blank -- a model-based algorithm

Question 72

Practical Guidelines for Algorithm Selection Synthesizing the detailed analysis of decision factors and trade-offs, this section provides actionable advice for selecting an RL algorithm based on specific problem characteristics. Which RL algorithm is universally superior?

Answer 72

It is crucial to reiterate that no single RL algorithm is universally superior; continuous advancements and the integration of different methods are common practices in the field.

Question 73

For real-world problems, especially those involving physical simulation, such as robotics, ___________ often emerges as the most critical factor due to the high cost and inherent slowness of data collection

Answer 73

sample efficiency

Question 74

Learning in the Real World (________Samples): If real-world learning is required and samples are extremely limited, ___________ RL approaches, such as Guided Policy Search (GPS), are frequently preferred due to their superior sample efficiency

Answer 74

first blank -- Expansive second blank -- Model-based

Question 75

Simulation Cost is a Factor ( __________ Cost): When simulation cost is a consideration but not prohibitive, _________ methods, including Q-learning and DDPG, are strong candidates. These methods often leverage experience replay to significantly improve sample efficiency

Answer 75

first blank -- Moderate Sample second blank -- Value-based

Question 76

Simulation Cost is a Factor (Moderate Sample Cost): When simulation cost is a consideration but not prohibitive, Value-based methods, including __________ and __________, are strong candidates. These methods often leverage experience replay to significantly improve __________________.

Answer 76

first blank -- Q-learning second blank -- DDPG third blank -- sample efficiency

Question 77

Learning in the Real World (Expensive Samples): If real-world learning is required and samples are extremely limited, Model-based RL approaches, such as _______________, are frequently preferred due to their superior _______________ .

Answer 77

first blank -- Guided Policy Search (GPS) second blank -- sample efficiency

Question 78

Low Simulation Cost (________ Samples): If sampling data is inexpensive, for example, in fast computer simulations, Policy Gradient or Actor-Critic methods like TRPO, PPO, and A3C become highly appealing. _________ are generally easier to set up and tend to be less sensitive to hyperparameters

Answer 78

first blank - Cheap second blank - Policy Gradient methods

Question 79

Low Simulation Cost (Cheap Samples): If sampling data is inexpensive, for example, in fast computer simulations, ________ or ____________ methods like TRPO, PPO, and A3C become highly appealing. Policy Gradient methods are generally easier to set up and tend to be less sensitive to hyperparameters

Answer 79

first blank -- Policy Gradient second blank -- Actor-Critic

Question 80

Low Simulation Cost (Cheap Samples): If sampling data is inexpensive, for example, in fast computer simulations, Policy Gradient or Actor-Critic methods like ____, ___, and ___ become highly appealing. Policy Gradient methods are generally easier to set up and tend to be less sensitive to hyperparameters

Answer 80

first blank -- TRPO second blank -- PPO third blank -- A3C

Question 81

RL Algorithm selection can be structured based on the ____________ or ______________ .

Answer 81

first blank -- cost of simulation second blank -- data acquisition

Question 82

Beyond this primary selection being based on the cost of simulation or data acquisition, How can the chosen approach be supplemented with additional methods?

Answer 82

For instance, one might add policy learning to a model-based RL framework (like GPS) or integrate value-learning components into a Policy Gradient algorithm t is also important to consider that the choice of algorithms can be sensitive to the specific task, particularly due to varying curvature characteristics in their reward functions, which often necessitates some empirical experimentation

Question 83

Sample efficiency ___________________ Definition/Core Concern

Question 84

Sample efficiency ___________________ Impact on Algorithm Choice (General Trends) Model-based:? Off-policy: ? On-policy: ? Evolutionary: ? (Lowest, Low, High, Highest)

Answer 84

Model-based: Highest (leverages dynamics). Off-policy: High (reuses samples). On-policy: Low (new samples per update). Evolutionary: Lowest.

Question 85

Sample efficiency ___________________ Key Considerations/Trade-offs

Answer 85

Trade-off with wall clock time: cheap samples favor model-free; expensive samples favor model-based.

Question 86

Stability & Convergence ___________________ Definition/Core Concern

Answer 86

Ease and speed of convergence; sensitivity to seeds/hyperparameters.

Question 87

Stability & Convergence ___________________ Impact on Algorithm Choice (General Trends) Value-fitting: ? Model-based:? Gradient:? (No guarantee, Generally converges, High variance)

Answer 87

Value-fitting: Problematic with deep nets (no guarantee, moving targets). Model-based: Generally converges (fits dynamics, but less generalized). Policy Gradient: High variance (can destroy training).

Question 88

Stability & Convergence ___________________ Key Considerations/Trade-offs

Answer 88

Improvements often add hyperparameters, hurting generalization. Local optima common.

Question 89

Generalization ___________________ Definition/Core Concern

Answer 89

Performance on unseen tasks/situations.

Question 90

Generalization ___________________ Impact on Algorithm Choice (General Trends) Model-based:? Model-free:? (Less generalizable, can generalize better)

Answer 90

Model-based: Less generalized (prone to errors in unfamiliar situations). Model-free: Can generalize better (learns complex policies directly).

Question 91

Generalization ___________________ Key Considerations/Trade-offs

Answer 91

Trade-off with model fidelity: explicit models are efficient but brittle; direct policies are adaptable but less efficient.

Question 92

Assumptions & Approximations ___________________ Definition/Core Concern

Answer 92

Underlying constraints (e.g., continuity, observability, action space type).

Question 93

Assumptions & Approximations ___________________ Impact on Algorithm Choice (General Trends) DQN: ? Q-learning: ? Policy Gradient: ? Value-fitting/Model-based: ? ( How does it handle? Discrete vs Continuous)

Answer 93

DQN: Discrete, low-dim control. Q-learning: Difficult for continuous. Policy Gradient: Supports both discrete/continuous. Value-fitting/Model-based: Often assume continuity.

Question 94

Assumptions & Approximations ___________________ Key Considerations/Trade-offs

Answer 94

Violating assumptions leads to failure. Problem structure dictates suitable algorithms.

Question 95

Exploration ___________________ Definition/Core Concern

Answer 95

Effectiveness in discovering states/actions.

Question 96

Exploration ___________________ Impact on Algorithm Choice (General Trends) Off-policy:? On-policy:? (More explicit control, Limited)

Answer 96

Off-policy: More explicit control (e.g., ε-greedy, replay buffer), broader understanding. On-policy: Limited, often relies on "hacks."

Question 97

Exploration ___________________ Key Considerations/Trade-offs

Answer 97

Violating assumptions leads to failure. Problem structure dictates suitable algorithms.

Question 98

Computational Simplicity & Parallelism ___________________ Definition/Core Concern

Answer 98

Ease of computation and ability to scale horizontally.

Question 99

Computational Simplicity & Parallelism ___________________ Impact on Algorithm Choice (General Trends) Model-based: ? Value-based/Model-based: ? Evolutionary: ? Model-free: ? (Computationally - cheap to expensive, Parallelism - easy to difficult )

Answer 99

Model-based: Computationally expensive (iterative methods). Value-based/Model-based: Hard to parallelize. Evolutionary: Low computation, highly parallel. Model-free: Appealing when samples are cheap.

Question 100

Computational Simplicity & Parallelism ___________________ Key Considerations/Trade-offs

Answer 100

High parallelism can reduce wall clock time even with low sample efficiency.

Question 101

Bias vs. Variance ___________________ Definition/Core Concern

Answer 101

Error from assumptions vs. sensitivity to data fluctuations.

Question 102

# Bias vs. Variance Bias vs. Variance ___________________ Impact on Algorithm Choice (General Trends) (How does variance affect it?) Monte-Carlo:? 1-step TD:? Policy Gradient:? Off-policy TD:?

Answer 102

Monte-Carlo: High variance, zero bias. 1-step TD: Low variance, biased. Policy Gradient: Vulnerable to variance. Off-policy TD: Lower variance, but hyperparameter sensitive

Question 103

Bias vs. Variance ___________________ Key Considerations/Trade-offs

Answer 103

Reducing variance often hurts sample efficiency; conflicting objectives.

Question 104

What is the importance of logging in RL?

Answer 104

--Debugging and Troubleshooting: RL agents often exhibit unpredictable behavior during training. --Detailed logs allow researchers to pinpoint when and why performance degrades, identifying issues such as exploding gradients, policy collapse, or environmental interaction errors. --Reproducibility: To ensure that experiments can be replicated, all relevant parameters, random seeds, and environmental interactions must be recorded. This is crucial for validating research findings and building upon previous work. --Performance Analysis: Tracking key metrics over time provides insights into the learning progress, convergence, and overall effectiveness of the agent. This includes monitoring rewards, losses, and other custom metrics. --Hyperparameter Tuning: RL algorithms are notoriously sensitive to hyperparameters. Logging the performance across different hyperparameter configurations is essential for systematic tuning and identifying optimal settings. --Comparison and Benchmarking: Consistent logging practices enable fair comparisons between different algorithms or variations of the same algorithm.

Question 105

What are the four things that should be monitored during an RL experiment?

Answer 105

Training Metrics, Environment Interactions, Agent State, System Information,

Question 106

What are the five parts to the comprehensive logging of Training Metrics?

Answer 106

--Episode Rewards: Total reward accumulated per episode. This is often the primary indicator of agent performance. --Episode Lengths: Number of steps per episode. --Losses: Policy loss, value loss, and any other relevant loss functions from the neural networks. --Learning Rates: Current learning rates for optimizers, especially if using schedules. --Gradient Norms: To detect exploding or vanishing gradients.

Question 107

What are the two parts to the comprehensive logging of Environment Interactions?

Answer 107

--Number of Steps/Frames: Total interactions with the environment. --Replay Buffer Size: For off-policy methods.

Question 108

What are the three parts to the comprehensive logging of Agent State?

Answer 108

--Hyperparameters: All hyperparameters used for the run (e.g., discount factor, GAE lambda, batch size, network architecture details). --Random Seeds: For reproducibility. --Model Checkpoints: Periodically save the agent's policy and value function weights.

Question 109

What are the three parts to the comprehensive logging of System Information?

Answer 109

--CPU/GPU Usage: Resource consumption during training. --Memory Usage: To identify potential leaks or inefficiencies. --Wall Clock Time: Total training duration.

Question 110

Name at least two common logging Tools/approaches for RL experiments

Answer 110

--Python's logging Module: For basic text-based logging to console and files. Useful for detailed step-by-step information. --TensorBoard: A powerful visualization tool from TensorFlow, widely adopted across various deep learning frameworks (including PyTorch). It allows for plotting scalars (rewards, losses), visualizing network graphs, embedding projections, and more. --Weights & Biases (W&B): A popular platform for experiment tracking, visualization, and collaboration. It offers more advanced features like hyperparameter sweeps, artifact management, and interactive dashboards. --MLflow: An open-source platform for managing the ML lifecycle, including experiment tracking, reproducible runs, and model deployment. --Custom File Logging: Simple CSV or JSON files can be used to store tabular data for later analysis, especially for smaller experiments.