Learning the Structure of Factored Markov Decision Processes in
... Networks (dbns). Classical solution methods (i.e. dp) have been successfully adapted to manipulate such representations (Boutilier et al., 2000) and have been developed to solve large problems (Hoey et al., 1999; Guestrin et al., 2003). However, these planning techniques require a perfect knowledge ...
... Networks (dbns). Classical solution methods (i.e. dp) have been successfully adapted to manipulate such representations (Boutilier et al., 2000) and have been developed to solve large problems (Hoey et al., 1999; Guestrin et al., 2003). However, these planning techniques require a perfect knowledge ...
האוניברסיטה העברית בירושלי - Center for the Study of Rationality
... the actions set, the participant may assume that there is only a single available action, the pressing of any button, regardless of its properties (Figure 1B). Alternatively, differences in the timing of the button press, the finger used, etcetera, could all define different actions. Such precise de ...
... the actions set, the participant may assume that there is only a single available action, the pressing of any button, regardless of its properties (Figure 1B). Alternatively, differences in the timing of the button press, the finger used, etcetera, could all define different actions. Such precise de ...
CSE 571: Artificial Intelligence
... Audio of [Sep 9, 2009] Online Search--motivations, methods; model incompleteness considerations; need for ergodicity of the environment. Connections to Reinforcement Learning. Audio of [Sep 11, 2009] Issues of Conformant and conditional planners searching in belief space. ...
... Audio of [Sep 9, 2009] Online Search--motivations, methods; model incompleteness considerations; need for ergodicity of the environment. Connections to Reinforcement Learning. Audio of [Sep 11, 2009] Issues of Conformant and conditional planners searching in belief space. ...
Reinforcement Learning and the Reward Engineering Principle
... Thus, depending on a posteriori facts about the environment, there may exist dominated policies that cannot be elicited from an omniscient reinforcement learner by any reward schedule. Trivially, dominated policies cannot be elicited from an omniscient reinforcement learner by any reward schedule (s ...
... Thus, depending on a posteriori facts about the environment, there may exist dominated policies that cannot be elicited from an omniscient reinforcement learner by any reward schedule. Trivially, dominated policies cannot be elicited from an omniscient reinforcement learner by any reward schedule (s ...
PowerPoint - University of Virginia, Department of Computer Science
... Overall goal is known, but lacking moment-to-moment performance measure • Don’t exactly know what performance maximizing action is at each step ...
... Overall goal is known, but lacking moment-to-moment performance measure • Don’t exactly know what performance maximizing action is at each step ...
Learning in Markov Games with Incomplete Information
... 2-player zero-sum Markovgame to a 2-player generalsum Markovgame. In a zero-sum game, two players’ rewards always sum to zero for any situation. That means one agent’s gain is always the other agent’s loss, thus agents have strictly opposite interests. In a general-sum game, agents’ rewards can sum ...
... 2-player zero-sum Markovgame to a 2-player generalsum Markovgame. In a zero-sum game, two players’ rewards always sum to zero for any situation. That means one agent’s gain is always the other agent’s loss, thus agents have strictly opposite interests. In a general-sum game, agents’ rewards can sum ...
Reinforcement Learning and the Reward
... of the ability to achieve goals in the world”;2 additionally, we find that they are often concerned with generality (or at least flexibility or adaptability) of these computational systems, as in Legg and Hutter’s definition: “Intelligence measures an agent’s ability to achieve goals in a wide rang ...
... of the ability to achieve goals in the world”;2 additionally, we find that they are often concerned with generality (or at least flexibility or adaptability) of these computational systems, as in Legg and Hutter’s definition: “Intelligence measures an agent’s ability to achieve goals in a wide rang ...
Object Focused Q-learning for Autonomous Agents
... modular reinforcement learning, even though we have different goals than modular RL. Russell & Zimdars [13] take into account the whole state space for the policy of each module, so they can obtain global optimality, at the expense of not addressing the dimensionality problems that we tackle. Spragu ...
... modular reinforcement learning, even though we have different goals than modular RL. Russell & Zimdars [13] take into account the whole state space for the policy of each module, so they can obtain global optimality, at the expense of not addressing the dimensionality problems that we tackle. Spragu ...
PDF
... rewards, values, and transition probabilities are explicitly modeled using lookup tables. In at , denoted by : i ˆ > +@ , the this representation, for each state , we store the expected reward ...
... rewards, values, and transition probabilities are explicitly modeled using lookup tables. In at , denoted by : i ˆ > +@ , the this representation, for each state , we store the expected reward ...
Poster - Department of Information Technology
... time-consuming process used to create metal products such as sheets. ...
... time-consuming process used to create metal products such as sheets. ...
Partial Reinforcement Schedules and Exercise
... frequency that he fed the rats. Instead of the rats' operant behavior decreasing, it remained stable even with the change in feeding schedule (Boeree, 1998). This "accident" led Skinner to his discovery of the four schedules of partial reinforcement: fixed ratio (FR), variable ratio (VR), fixed inte ...
... frequency that he fed the rats. Instead of the rats' operant behavior decreasing, it remained stable even with the change in feeding schedule (Boeree, 1998). This "accident" led Skinner to his discovery of the four schedules of partial reinforcement: fixed ratio (FR), variable ratio (VR), fixed inte ...
Reinforcement Schedules (Operant Conditioning)
... A professional baseball player gets a hit approximately every third time at bat A charitable organization makes an average of ten phone calls for every donation it receives Fixed Interval: moderate response rates; flurry of activity towards the end of each interval; “Scooping” – animals & peo ...
... A professional baseball player gets a hit approximately every third time at bat A charitable organization makes an average of ten phone calls for every donation it receives Fixed Interval: moderate response rates; flurry of activity towards the end of each interval; “Scooping” – animals & peo ...
Introduction to Machine Learning. - Electrical & Computer Engineering
... A Representation for Learned Function – Vˆ b w 0 w 1bp b w 2 rp b w 3 bk b w 4 rk b w 5 bt b w 6 rt b – bp/rp = number of black/red pieces; bk/rk = number of black/red kings; bt/rt = number of black/red pieces threatened (can be taken on next turn) ...
... A Representation for Learned Function – Vˆ b w 0 w 1bp b w 2 rp b w 3 bk b w 4 rk b w 5 bt b w 6 rt b – bp/rp = number of black/red pieces; bk/rk = number of black/red kings; bt/rt = number of black/red pieces threatened (can be taken on next turn) ...
stairs 2012 - Shiwali Mohan
... the agent fails to finish the game quickly. Players have to often choose between competing goals and make decisions that can give favorable long term rewards. In this work, we concentrate on designing a reinforcement learning agent for a variant of a popular action game, Super Mario Bros, called Inf ...
... the agent fails to finish the game quickly. Players have to often choose between competing goals and make decisions that can give favorable long term rewards. In this work, we concentrate on designing a reinforcement learning agent for a variant of a popular action game, Super Mario Bros, called Inf ...
Learning how to Learn Learning Algorithms: Recursive Self
... prover: either formula is true but unprovable, or math is flawed in an algorithmic sense Universal problem solver Gödel machine uses self reference trick in a new way ...
... prover: either formula is true but unprovable, or math is flawed in an algorithmic sense Universal problem solver Gödel machine uses self reference trick in a new way ...
CE213 Artificial Intelligence – Revision
... 1. General AI approach to problem solving: “generate/try + evaluate/test” (actions/solutions) 2. Problem formalisation and knowledge/solution representation: state-action pairs/mapping, sequence of actions/moves, input-output mapping (rules, decision tree, neural net), 3. Search strategies and evalu ...
... 1. General AI approach to problem solving: “generate/try + evaluate/test” (actions/solutions) 2. Problem formalisation and knowledge/solution representation: state-action pairs/mapping, sequence of actions/moves, input-output mapping (rules, decision tree, neural net), 3. Search strategies and evalu ...
Paper
... The MDP model is a formal specification for planning under uncertainty originally developed in the OR community in the late 50s and early 60s. The foundational work was done by Bellman (1957) and Howard (1960), and included a formal description of the model and basic results such as the existence of ...
... The MDP model is a formal specification for planning under uncertainty originally developed in the OR community in the late 50s and early 60s. The foundational work was done by Bellman (1957) and Howard (1960), and included a formal description of the model and basic results such as the existence of ...
Igor Kiselev - University of Waterloo
... more simple and more complex algorithms (e.g. AWESOME [Conitzer and Sandholm 2003]) Classification of situations (games) with various values of the delta and alpha variables: what values are good in what situations. Extending work to have more players. Online learning and exploration policy in stoch ...
... more simple and more complex algorithms (e.g. AWESOME [Conitzer and Sandholm 2003]) Classification of situations (games) with various values of the delta and alpha variables: what values are good in what situations. Extending work to have more players. Online learning and exploration policy in stoch ...
Module Descriptor 2012/13 School of Computer Science and Statistics.
... Have a thorough understanding of the development of autonomous agents that are aware of their environment, can react to external stimuli, can behave according to sets of rules defined by a game designer, and learn automatically from interaction with the game environment Be able to represent knowledg ...
... Have a thorough understanding of the development of autonomous agents that are aware of their environment, can react to external stimuli, can behave according to sets of rules defined by a game designer, and learn automatically from interaction with the game environment Be able to represent knowledg ...
INTELLIGENT AGENT PLANNING WITH QUASI
... algorithm by J.A. Martin [11] was used. For the initial positions where the first approach began to fail, and also for the initial position of x = -0.5, which is the “standard” start point suggested by the problem author(s), a comparison was made in terms of the number of time steps of the solution. ...
... algorithm by J.A. Martin [11] was used. For the initial positions where the first approach began to fail, and also for the initial position of x = -0.5, which is the “standard” start point suggested by the problem author(s), a comparison was made in terms of the number of time steps of the solution. ...
Learning Agents - University of Connecticut
... Policy: a mapping from states to actions Policy is as opposed to action sequence Agents that precompute action sequences cannot respond to new sensory information Agent that follows a policy incorporates sensory information about state into action determination ...
... Policy: a mapping from states to actions Policy is as opposed to action sequence Agents that precompute action sequences cannot respond to new sensory information Agent that follows a policy incorporates sensory information about state into action determination ...