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Related Concept Videos

Reinforcement01:23

Reinforcement

Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
Reinforcement Schedules01:24

Reinforcement Schedules

Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a bonus...
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Law of Effect

B.F. Skinner, a prominent figure in behavioral psychology, introduced operant conditioning by emphasizing the role of consequences in shaping behavior. This theory builds upon the law of effect proposed by Edward Thorndike, which posits that behaviors followed by satisfying outcomes are likely to be repeated. In contrast, those followed by unsatisfying outcomes are less likely to recur.
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Operant Conditioning Intervention01:24

Operant Conditioning Intervention

Operant conditioning serves as a foundational principle in therapeutic interventions aimed at modifying maladaptive behaviors. Central to this approach is the notion that behaviors, both adaptive and maladaptive, are learned through reinforcement. By analyzing the environmental factors that reinforce problematic behaviors, clinicians can design interventions to weaken these reinforcements and replace maladaptive behaviors with healthier alternatives.
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Pavlovian Conditioned Approach Training in Rats
06:57

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Published on: February 4, 2016

Learning to trade via direct reinforcement.

J Moody1, M Saffell

  • 1Computational Finance Program, Oregon Graduate Institute of Science and Technology, Beaverton, OR 97006, USA.

IEEE Transactions on Neural Networks
|February 6, 2008
PubMed
Summary

We developed a new recurrent reinforcement learning (RRL) method for direct reinforcement (DR) in investment strategies. This approach optimizes portfolios and trading systems, outperforming traditional methods like Q-learning.

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Area of Science:

  • Quantitative Finance
  • Machine Learning
  • Algorithmic Trading

Background:

  • Traditional investment optimization often relies on forecasting models and value function estimation.
  • Methods like dynamic programming, TD-learning, and Q-learning face challenges such as Bellman's curse of dimensionality.

Purpose of the Study:

  • To introduce a novel direct reinforcement (DR) approach for optimizing investment portfolios, asset allocations, and trading systems.
  • To present the recurrent reinforcement learning (RRL) algorithm as a method for discovering investment policies directly.
  • To demonstrate the advantages of DR over value function-based methods in financial applications.

Main Methods:

  • Utilized a direct reinforcement (DR) framework, treating investment decision-making as a stochastic control problem.
  • Developed and applied the recurrent reinforcement learning (RRL) algorithm to discover investment policies.
  • Optimized risk-adjusted investment returns, including the differential Sharpe ratio, while incorporating transaction costs.

Main Results:

  • The RRL-based DR approach eliminates the need for separate forecasting models, leading to improved trading performance.
  • RRL offers a simpler problem representation and avoids Bellman's curse of dimensionality, enhancing computational efficiency.
  • Simulations using real financial data showed RRL-based strategies outperformed Q-learning systems.

Conclusions:

  • Direct reinforcement learning, particularly with the RRL algorithm, provides a more efficient and effective method for investment optimization.
  • The RRL framework demonstrates practical applicability in real-world scenarios, such as intra-daily currency trading and monthly S&P 500 asset allocation.