Reinforcement Learning

Alternative framework for agent behavior; based on reward maximization rather than free energy minimization.

Neighborhood — ranked by edge-count

paper

thinker

Andrew Barto
introduces
Co-author of reinforcement learning and intrinsic motivation theories.
Richard Sutton
introduces
Co-author of reinforcement learning framework.

method

Q-learning
cites
Model-free RL algorithm used in experimental comparison; employs ε-greedy exploration.

concept

Active Inference, Curiosity and Insight (Friston et al., 2017)
mentions
The primary source paper being extracted
Temporal Difference Error
implements
The formal RL quantity whose signed character is identified with valence in the paper's account

claim

Natural exploration-exploitation trade-offs emerge automatically from expected free energy minimization without hyperparameter tuning.
contradicts
Active inference achieves Bayes-optimal arbitration between exploration and exploitation without handcrafted mechanisms like ε-greedy.

framework

Reinforcement Learning from AI Feedback
related_to
Variant of RLHF where human feedback is replaced with AI-generated feedback for harmlessness.
Active Inference
contradicts
Foundational framework by Karl Friston; the paper extends it to three hierarchical levels for modeling meta-awareness.
Bayesian Model-Based Reinforcement Learning
cites
RL variant that maintains beliefs over environment model; compared to active inference using Thompson sampling.

2-hop · via this framework's ideas

Where ideas in this framework connect to the rest of the corpus — the same concept, an analogy, or a restatement elsewhere.

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Deep Reinforcement Learningmethod0.902
AI training method inspired by behaviorism, used for autonomous cars and drones; cited as bioinspired success
Reinforcement learning (RL)concept0.899
Machine learning paradigm where agents learn to maximize cumulative reward through interaction.
Reinforcement Learning from Human Feedbackmethod0.887
Method for fine-tuning LMs based on human preferences; mentioned as combining RL and LMs.
Inverse Reinforcement Learningmethod0.868
Value learning method inferring reward function from expert demonstrations; reviewed as insufficient for superintelligent alignment
Reinforcement learning of tissuesconcept0.862
The hypothesis that cellular collectives can be trained via rewards/punishments to produce specific morphological outcomes.
Reinforcement Learning for Tissuesmethod0.859
Proposed experimental paradigm to train morphogenesis using rewards and punishments, treating tissues as learning agents.
"reinforcement learning is learning what to do – how to map situations to actions – so as to maximize a numerical reward signal"quote0.857
Operational definition of RL used throughout the paper, quoted from Sutton.
Monte-Carlo reinforcement learningmethod0.850
Reinforcement learning methods that update parameters at the end of an episode based on sampled returns.