The generalized version of policy improvement and policy evaluation allows one to leverage the solution of some tasks to speed up the solution of others. If the reward function of a task can be well approximated as a linear combination of the reward functions of tasks previously solved, we can reduce a reinforcement-learning problem to a simpler linear regression. When this is not the case, the agent can still exploit the task solutions by using them to interact with and learn about the environment. Both strategies considerably reduce the amount of data needed to solve a reinforcement-learning problem.
The combination of reinforcement learning with deep learning
is a promising approach to tackle important sequential decisionmaking
problems that are currently intractable. One obstacle to
overcome is the amount of data needed by learning systems of
this type. This article proposes to address this issue through
a divide-and-conquer approach. The authors argue that complex decision
problems can be naturally decomposed into multiple tasks that
unfold in sequence or in parallel. By associating each task with
a reward function, this problem decomposition can be seamlessly
accommodated through a generalization
of two fundamental operations in reinforcement learning:
policy improvement and policy evaluation.
In this article we forecast daily closing price series of Bitcoin, Litecoin and Ethereum cryptocurrencies, using data on prices and volumes of prior days. Cryptocurrencies price behaviour is still largely unexplored, presenting new opportunities for researchers and economists to highlight similarities and differences with standard financial prices. We compared our results with various benchmarks: one recent work on Bitcoin prices forecasting that follows different approaches, a well-known paper that uses Intel, National Bank shares and Microsoft daily NASDAQ closing prices spanning a 3-year interval and another, more recent paper which gives quantitative results on stock market index predictions. We followed different approaches in parallel, implementing both statistical techniques and machine learning algorithms: the Simple Linear Regression (SLR) model for uni-variate series forecast using only closing prices, and the Multiple Linear Regression (MLR) model for multivariate series using both price and volume data. We used two artificial neural networks as well: Multilayer Perceptron (MLP) and Long short-term memory (LSTM). While the entire time series resulted to be indistinguishable from a random walk, the partitioning of datasets into shorter sequences, representing different price ‘‘regimes’’, allows to obtain precise forecast as evaluated in terms of Mean Absolute Percentage Error(MAPE) and relative Root Mean Square Error (relativeRMSE). In this case the best results are obtained using more than one previous price, thus confirming the existence of time regimes different from random walks. Our models perform well also in terms of time complexity, and provide overall results better than those obtained in the benchmark studies, improving the state-of-the-art.
The key difference in distributional reinforcement learning (RL) lies in how ‘anticipated reward’ is defined. In traditional RL, the reward prediction is represented as a single quantity: the average taken over all potential reward outcomes, weighted by their respective probabilities. By contrast, distributional RL uses a multiplicity of predictions. These predictions vary in their degree of optimism about upcoming reward. More optimistic predictions anticipate obtaining greater future rewards; less optimistic predictions anticipate less positive outcomes. Together, the entire range of predictions captures the full probability distribution over future rewards.
A person's mood has been linked with predictions of future reward and it has been proposed that both depression and bipolar disorder may involve biased predictions of future value. These biases may arise from asymmetries in reward prediction error (RPE) coding.
Much of systems neuroscience has attempted to formulate succinct statements about the function of individual neurons in the brain. This approach has been successful at explaining some (relatively small) circuits and certain hard-wired behaviours. However, there is reason to believe that this approach will need to be complemented by other insights if we are to develop good models of plastic circuits with thousands, millions or billions of neurons. There is, unfortunately, no guarantee that the function of individual neurons in the CNS can be compressed down to a human-interpretable, verbally articulable form. Given that we currently have no good means of distilling the function of individual units in deep ANNs into words, and given that real brains are likely more, not less, complex, we suggest that systems neuroscience would benefit from focusing on the kinds of models that have been successful in ANN research programs, i.e., models grounded in the three essential components: objective functions, the learning rules and the architectures.
a lot of computational neuroscience has emphasized models of the dynamics of neural activity, which has not been a major theme in this discussion. As such, one might worry that the framework fails to connect with this past literature.