beginner
9 min read
Compare Greedy and Exploratory Strategies in a Contextual Bandit With Python
A greedy agent looks brilliant for the first fifty rounds. Then it stops learning, because it stopped asking questions.
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Exploration
How agents seek information or discover useful behavior, including exploration strategies, bonuses, uncertainty, and intrinsic motivation.
Tagged articles
Exploration articles
16 articles in this tag.
beginner
9 min read
Contextual Bandits vs Reinforcement Learning: When Actions Don’t Shape Future States
Picture a system that picks which article to show a returning reader. It looks at what it knows about them—their reading history, the time of day, the…
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advanced
9 min read
Continuous-Action Policies in Reinforcement Learning: Distributions, Bounds, and Noise
You move from discrete actions to continuous control, wire up a Gaussian policy head, and the agent either jitters uselessly around a target or slams…
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advanced
10 min read
Deterministic Policy Gradients Explained: Learning Continuous Actions Without Sampling a Distribution
A stochastic policy averages over actions. A deterministic policy climbs the critic's slope at the single action it outputs. That swap—from averaging to…
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advanced
11 min read
Evaluating Exploration in RL: Did the Agent Discover Useful Information?
A novelty bonus can make your agent look busy without making it look smart. The intrinsic-reward curve climbs, the state-visitation heatmap fills in, and…
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intermediate
7 min read
Compare Count and Uncertainty Bonuses in a Small Reinforcement Learning Task
You add a curiosity bonus. Coverage climbs. The return curve goes flat. That gap is the whole lesson: a bonus is a second reward channel, and the only…
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advanced
11 min read
Exploration Bonuses in RL: Count, Novelty, Prediction Error, and Uncertainty
Epsilon-greedy exploration is randomness without a reason. An exploration bonus is randomness with a budget and a purpose: it changes the reward signal…
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beginner
14 min read
Exploration vs Exploitation in Reinforcement Learning
Your agent is stuck in a loop. It found one action that earns a small reward, and now it repeats that action forever—never discovering that a much better…
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intermediate
8 min read
Test When Curiosity Rewards Distract an RL Agent
Watch the two curves long enough and you will see the moment curiosity stops working for the agent and starts working for itself. The intrinsic bonus keeps…
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advanced
11 min read
Intrinsic Motivation in Reinforcement Learning: Exploring When External Rewards Are Sparse
Curiosity is a loan of learning signal. It accelerates exploration when the borrowed interest points toward the goal—and it becomes a debt when the agent…
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intermediate
11 min read
Optimism and Confidence Bonuses in Tabular Reinforcement Learning
An agent that has seen an action once does not know its value. It knows one sample. Optimism is the discipline of treating thin evidence as a reason to…
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intermediate
11 min read
Q-Learning Explained: How Off-Policy Value Learning Finds Better Actions
You can recite the Q-learning update. Maybe you've even coded it in a toy grid world and watched the agent stumble toward a goal. But if someone asked you…
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beginner
12 min read
Reinforcement Learning vs Supervised Learning: How the Feedback Loop Changes
A reward is not a delayed label. That single confusion sits behind most of the trouble beginners have telling supervised learning and reinforcement…
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advanced
11 min read
Safe Exploration in Reinforcement Learning: Action Shields, Rules, and Fallbacks
Your agent proposes an action that crosses a hard safety boundary. You can block it, replace it, or fall back to a known-safe controller. The naive…
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advanced
11 min read
Soft Actor-Critic Explained: Entropy-Regularized Learning for Continuous Control
Most actor-critic methods force you to choose between sample efficiency and stable exploration. Soft Actor-Critic (SAC) refuses the trade—and that refusal…
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beginner
9 min read
A Small Tabular RL Experiment: Watch Exploration Become a Policy
A Q-table makes the invisible visible. Before neural networks, before GPUs, before thousands of parameters, there is a simple grid, a handful of numbers,…
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