Roger the robot :)


--- # Contents * [Introduction](#background) * [Agent-environment interaction](#agent-env) * [Reinforcement...](#reinforcement) * [...Learning](#learning) * [AI\\(\xi\\)](#aixi) * [Optimality](#optimality) * [Agents](#agents) * [Knowledge-seeking agents](#ksa) * [BayesExp](#bexp) * [Thompson Sampling](#ts) * [Optimistic AIXI](#opt-aixi) * [MDL Agent](#mdl) * [References and further reading](#further-reading) # Introduction AIXI and its variants are theoretical models of general intelligence: if we make minimal assumptions about our environment, what constitutes rational behavior, if computation weren't an issue? For simple domains such as playing board games like Chess and Go, optimal play is trivial if you have infinite computing power: just use [minimax]( In the general case of decision-making under uncertainty and with only partial knowledge, however, things aren't that simple. Environments can have traps from which it can be impossible to recover. Without being told in advance which actions lead to traps and which actions lead to treasure, an agent will have a tough time learning and performing optimally in such environments. This demo shows some of the properties of Bayesian agents in general environments. Read on! ## Agent-environment interaction In the standard [reinforcement learning] (RL) framework, the __agent__ and __environment__ play a turn-based game and interact in cycles ([Sutton & Barto, 1998]). At time/cycle/turn \\(t\\), the agent supplies the environment with an __action__ \\(a\_t\\). The environment then performs some computation and returns a __percept__ \\(e_t\\) to the agent, and the cycle repeats.

agent-environment interaction

The actions live in an action space \\(\mathcal{A}\\), and the percepts live in a percept space \\(\mathcal{E}\\). We identify an agent with its __policy__, which in general is a distribution over actions \\(\pi(a\_t\lvert ae\_{<t})\\) $$ \pi\ :\ \left(\mathcal{A}\times\mathcal{E}\right)^*\mapsto\varDelta\mathcal{A}, $$ where \\(^*\\) is the [Kleene star], and \\(\varDelta \mathcal{X}\\) is the set of probability measures over \\(\mathcal{X}\\). An environment is a distribution over percepts \\(\nu(e\_t\lvert ae\_{<t}a\_t)\\) with $$ \nu\ :\ \left(\mathcal{A}\times\mathcal{E}\right)^*\times\mathcal{A}\mapsto\varDelta\mathcal{E}. $$ The agent and environment interaction induces a distribution over __histories__ \\(\nu^\pi\\): $$ \nu^\pi\left(ae\_{<t}\right) \stackrel{.}{=}\prod\_{k=1}^{t}\pi\left(a\_k\lvert ae\_{<t}\right)\nu\left(e\_k\lvert ae\_{<k}a\_k\right), $$ which is conveniently expressed as a telescoping product by the [chain rule]. In the general setting, the environment is a [partially observable Markov decision process][POMDP] (POMDP). That is, there is some underlying (hidden) state space \\(\mathcal{S}\\), with respect to which the environment's dynamics are Markovian. The agent cannot observe this state directly, but instead receives (incomplete and noisy) percepts through its sensors. Therefore, the agent must learn and make decisions under uncertainty in order to perform well. Notes: * We're slightly abusing notation here: environments are _not_ joint distributions over actions and percepts, and so you should read \\(\nu(e\_t\lvert ae\_{<t}a\_t)\\) as shorthand for \\(\nu(e\_t\lvert e\_{<t} \lvert\lvert a_{1:t})\\); that is, a distribution over percepts conditioned on a sequence of past percepts, and in the context of a sequence of past actions given as _inputs_. The same holds for policies, except with actions and percepts reversed. * We restrict ourselves to countable percept and action spaces. Although many (but not all) of the important results generalize to continuous spaces, we make this assumption for simplicity. * For simplicity, we also assume that the action and percept spaces \\(\mathcal{A}\\) and \\(\mathcal{E}\\) are stationary; i.e. they are time-independent and fixed by the environment. ## Reinforcement... Percepts consist of (__observation__, __reward__) pairs \\(e\_k = \left(o\_k,r\_k\right)\\), with integer-valued rewards such that $$ \mathcal{E} = \mathcal{O}\times\mathbb{Z}. $$ We make no further assumptions about the observation space \\(\mathcal{O}\\). A good example is the space of \\(M\times N\\)-pixel 8-bit RGB images used in the [Arcade Learning Environment][ALE] (ALE): $$ \mathcal{O}_{\text{ALE}}=\mathcal{B}^{8\times M\times N\times 3}, $$ where \\(\mathcal{B}\stackrel{.}{=}\lbrace{0,1\rbrace}\\) is the binary alphabet. Now, introduce the __return__, which is the discounted sum of all future rewards: $$ R\_t \stackrel{.}{=} \sum\_{k=t}^{\infty}\gamma\_k r\_k, $$ where \\(\gamma\ :\ \mathbb{N}\mapsto[0,1]\\) is a discount function with convergent sum. Now, if our agent is rational in the [Von Neumann-Morgenstern][VNM] sense, it should maximize the expected return, which we call the __value__. The value achieved by policy \\(\pi\\) in environment \\(\nu\\) given history \\(ae\_{<t}\\) is defined as $$ V^{\pi}\_{\nu}\left(ae\_{<t}\right)\stackrel{.}{=}\mathbb{E}^{\pi}\_{\nu}\left[\left.\sum\_{k=t}^{\infty}\gamma\_{k}r\_{k}\right|ae\_{<t}\right] $$ This is often more conveniently expressed recursively: $$ V^{\pi}\_{\nu}\left(ae\_{<t}\right) = \sum\_{a\_t\in\mathcal{A}}\pi(a\_t\lvert ae\_{<t})\sum\_{e\_t\in\mathcal{E}}\nu(e\_t\lvert ae\_{<t}a\_t)\Big[\gamma\_tr\_t+\gamma\_{t+1}V\_{\nu}^{\pi}(ae\_{1:t})\Big], $$ which is often referred to as the [Bellman equation]. Now, let \\(\mu\\) be the true environment. The __optimal value__ is the highest value achieved by any policy in this environment: $$ V\_{\mu}^{*}\stackrel{.}{=}\max\_{\pi}V\_{\mu}^{\pi}. $$ Using the [distributive property] of \\(\max\\) and \\(\sum\\), we can unroll this into the __expectimax__ expression $$ V\_{\mu}^{*}=\lim\_{m\to\infty}\max\_{a\_{t}\in\mathcal{A}}\sum\_{e\_{t}\in\mathcal{E}}\cdots\max\_{a\_{m}\in\mathcal{A}}\sum\_{e\_{m}\in\mathcal{E}}\sum\_{k=t}^{m}\gamma\_{k}r\_{k}\prod\_{j=t}^{k}\mu\left(e\_{j}\lvert ae\_{<j}a\_{j}\right). $$ This can be viewed as just a generalization of [minimax] to stochastic environments/adversaries. In practice, when planning we approximate this computation with [Monte Carlo tree search] (MCTS). We can now introduce our first theoretical [artificial general intelligent] (AGI) agent: the __informed agent AI__\\(\boldsymbol{\mu}\\). AI\\(\mu\\) is simply the (infeasible) \\(\boldsymbol{\mu}\\)__-optimal policy__: $$ \pi^{\text{AI}\mu}\stackrel{.}{=}\arg\max\_{\pi}V\_{\mu}^{\pi}. $$ ## ...Learning Clearly we don't know \\(\mu\\) _a priori_ in the general RL setting. Generically, there are two main approaches to learning in the context of RL: __model-based__ and __model-free__. They each make their own sets of assumptions: * Model-free (e.g. [Q-Learning]) generally assume the environment is a finite-state MDP * Model-based (e.g. __Bayesian learning__) assumes the __realizable case__. The agents we'll be dealing with are all Bayesian. ## Bayesian reinforcement learning Assume the realizable case: the true environment \\(\mu\\) is contained in some countable __model class__ \\(\mathcal{M}\\). Now, constructor a __Bayesian mixture__ over \\(\mathcal{M}\\): a [convex linear combination] of environments: $$ \xi\left(e\_t\lvert ae\_{<t}a\_t\right)\stackrel{.}{=}\sum\_{\nu\in\mathcal{M}}w\_\nu \nu\left(e\_t\lvert ae\_{<t}a\_t\right). $$ The weights \\(w\_\nu\equiv\Pr\left(\nu\lvert ae\_{<t}\right)\\) specify the agent's __posterior belief distribution__ over \\(\mathcal{M}\\). By [Cromwell's rule], we require further that the __prior__ weights \\(\Pr(\nu\lvert \epsilon)\\) lie in the interval \\((0,1)\ \forall \nu\in\mathcal{M}\\). Being 'Bayesian' simply means __updating__ these beliefs according to the product rule of probability: $$ \Pr(\nu\lvert e\_t) = \frac{\Pr(e\_t\lvert \nu)\Pr(\nu)}{\Pr(e\_t)}, $$ which corresponds to performing the update at each cycle: $$ w\_\nu\leftarrow\frac{\nu(e\_t)w\_\nu}{\xi(e\_t)}. $$ ## AI\\(\xi\\) AI\\(\xi\\) is the __Bayes-optimal__ agent. That is, it is the policy that maximizes the \\(\xi\\)-expected return: $$ \pi^{\text{AI}\xi}\stackrel{.}{=}\arg\max\_\pi V^\pi\_\xi. $$ It is a universal, parameter-free Bayesian agent, whose behavior is completely specified by its model class \\(\mathcal{M}\\) and choice of prior \\(\lbrace w\_\nu\rbrace\_{\nu\in\mathcal{M}}\\). AI\\(\xi\\)'s big brother is [AIXI] ([Hutter, 2005]), which uses [Solomonoff's universal prior], which mixes over the model class of all computable probability measures: $$ w\_{\nu} = 2^{-K(\nu)}, $$ where \\(K(\nu)\\) is the [Kolmogorov complexity] of \\(\nu\\). AIXI is hence the 'active' generalization of Solomonoff induction, which is the optimal (but incomputable) inductive learner. A computable approximation to AIXI is MC-AIXI-CTW ([Veness et al., 2011]), which uses the [Context Tree Weighting] (CTW) algorithm to approximate the induction component of AIXI, and a variant of the UCT MCTS algorithm for planning (Kocsis & Szepasvari, 2006). AIXI is known to have the property of _on-policy value convergence_; that is: $$ V^{\pi}\_{\xi} - V^{\pi}\_{\mu} \to 0 \quad\text{as }t\to\infty. $$ This means that AIXI learns how good its policy is, asymptotically. Unfortunately, this is not the same as _asymptotic optimality_, a property that AIXI indeed lacks: $$ V^{\star}\_{\mu} - V^{\pi}\_{\mu} \to 0 \quad\text{as }t\to\infty. $$ ## Exploration vs exploitation; optimality The most central issue in reinforcement learning is the __exploration-exploitation dilemma__: how do you know that what you're doing is the best use of your time? When, and how often, should you take a break from exploiting the best policy you currently know, to go and explore, in the hope of learning more about the environment and discovering a better policy? Also, when you _do_ decide to explore, what strategy should you use to learn the most, given your incomplete knowledge of the environment? These are some of the (many, and big) open problems in the theory of reinforcement learning. It is now known that [Pareto optimality] is trivial in general environments, and that AIXI can be made to perform arbitrarily badly with a dogmatic prior ([Leike & Hutter, 2015]). # Agent zoo Now, let's introduce AIXI's cousins: knowledge-seeking agents, Thompson sampling, MDL, BayesExp, and Optimistic AIXI. They are all attempts to address the issues with the 'vanilla' Bayesian agent. ## Knowledge-seeking agents First generalize to Bayesian __utility agents__ that have, in addition to a belief distribution \\(\xi\\), a utility function $$ u(e\_t\lvert ae\_{<t}a\_t) $$ which takes the place of the agent's reward signal. A standard reinforcement learner can be expressed as a utility agent by simply setting \\(u(e\_t)=r\_t\\), i.e. to pluck out the reward component of the percept signal. In this setting, the agent is an __expected utility maximizer__, with \\(u(e\_t)\\) replacing \\(r\_t\\) in its value function. Using this formalism, we can construct agents that (in principle, and absent any [self-delusions]( care about objective states of the world, and not about some arbitrary reward signal. Generically, knowledge-seeking agents (KSA; [Orseau, 2011]; [Orseau et al., 2013]) get their utility from seeing information. In the context of Bayesian agents, we express this in terms of their model. There are three KSA of note: * Square-KSA: $$ u(e\_t\lvert ae\_{< t} a\_t) = -\xi(e\_t\lvert ae\_{<t}) $$ * Shannon-KSA: $$ u(e\_t\lvert ae\_{< t} a\_t) = \log\left(\xi(e\_t\lvert ae\_{<t})\right) $$ * KL-KSA: $$ u(e\_t\lvert ae\_{< t} a\_t) = \text{Ent}(w\lvert ae\_{1:t}) - \text{Ent}(w\lvert ae\_{< t} a\_t) $$ ## BayesExp TODO ## Thompson Sampling Define the \\(\varepsilon\\)-effective horizon: $$ H\_{t}^{\gamma}(\varepsilon) \stackrel{.}{=} \min\left\lbrace m\ :\ \sum\_{k=m}^{\infty}\gamma\_{t+k}\leq\varepsilon\right\rbrace, $$ which is the minimum number of cycles ahead one needs to consider in order to accumulate a value of \\((1-\varepsilon)V\_{\nu}^{\pi}\\), for some \\(\varepsilon>0\\). Thompson Sampling ([Leike et al., 2016]) has a simple algorithm: every \\(H\_{t}^{\gamma}\\) steps, sample an environment \\(\rho\\) from the posterior belief distribution \\(w\_{\nu}\\), and use the \\(\rho\\)-optimal policy, re-sample, and repeat. ## Optimistic AIXI Optimistic AIXI ([Sunehag & Hutter, 2015]). TODO ## Minimum Description Length (MDL) Agent Where AIXI uses a mixture model -- in which it models the world as a weighted average of hypotheses and acts accordingly -- the MDL agent greedily chooses to model the world using the simplest plausible model it knows of. More formally, it chooses the \\(\rho\\)-optimal policy, where $$ \rho = \arg\min\_{\nu\in\mathcal{M}\ :\ w\_\nu > 0} K(\nu). $$ Notice the requirement that \\(w\_\nu > 0\\). This is a clue that the MDL agent will fail in stochastic environments, since its criterion for 'falsification' is too strict: it requires that its posterior assign exactly zero mass to some hypothesis before it's happy to move on and consider an alternative hypothesis. --- ## References & Further Reading [reinforcement learning]: [Kolmogorov complexity]: [Q-Learning]: [AIXI]: [Solomonoff's universal prior]: [VNM]: [minimax]: [Kleene star]: [convex linear combination]: [Cromwell's rule]: [Bellman equation]: [Monte Carlo tree search]: [distributive property]: [POMDP]: [chain rule]: [ALE]: [artificial general intelligent]: [Context Tree Weighting]: [Pareto optimality]: [Andrej Karpathy]: [REINFORCEjs]: [Hutter, 2005]: [Leike & Hutter, 2015]: [Leike et al., 2016]: [Veness et al., 2011]: [Orseau, 2011]: [Orseau et al., 2013]: [Sutton & Barto, 1998]: [Sunehag & Hutter, 2015]: