There is provided an information processing apparatus including a reward estimator generator using action history data, including state data expressing a state, action data expressing an action taken by an agent, and a reward value expressing a reward obtained as a result of the action, as learning data to generate, through machine learning, a reward estimator estimating a reward value from inputted state data and action data. The reward estimator generator includes: a basis function generator generating a plurality of basis functions; a feature amount vector calculator calculating feature amount vectors by inputting state data and action data in the action history data into the basis functions; and an estimation function calculator calculating an estimation function estimating the reward value included in the action history data from the feature amount vectors according to regressive/discriminative learning. The reward estimator includes the plurality of basis functions and the estimation function.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine learning apparatus comprising: a central processing unit (CPU) operable to: learn from action history data that includes state data, action data, and reward value, wherein the state data expressing a state of an agent, the action data expressing an action taken by the agent in the state, and the reward value expressing a reward obtained by the agent as a result of the action; estimate a plurality of reward values based on current state data and a plurality of action data expressing actions that can be taken next by the agent; select, among the actions, one action that has a highest estimated reward value; and execute the action that has a highest estimated reward value. 2. The machine learning apparatus according to claim 1, wherein the CPU is operable to: generate a reward estimator that estimates the reward value from the inputted state data and the action data; and update the reward estimator with additional state data, action data, and reward value added to the action history data. 3. The machine learning apparatus according to claim 2, wherein the CPU is operable to: generate a plurality of basis functions by combining a plurality of processing functions; calculate feature amount vectors by inputting the state data and the action data into the plurality of basis functions; calculate an estimation function that estimates the reward value from the feature amount vectors according to regressive/discriminative learning; and generate the reward estimator that includes the plurality of basis functions and the estimation function. 4. The machine learning apparatus according to claim 3, wherein the CPU is operable to: calculate feature amount vectors for all of the state data and the action data included in the action history data when a set of the state data, the action data, and the reward value has been added to the action history data; and sample the sets of the state data, the action data, and the reward value included in the action history data so that a distribution of coordinate points indicated by the feature amount vectors in a feature amount space approaches a specified distribution. 5. The machine learning apparatus according to claim 4, wherein the CPU is operable to set weightings for the sets of the state data, the action data, and the reward value included in the action history data so that the distribution of coordinate points indicated by the feature amount vectors in a feature amount space for the sets of the state data, the action data, and the reward value remaining after sampling approaches a specified distribution. 6. The machine learning apparatus according to claim 3, wherein the CPU is operable to: calculate feature amount vectors for all of the state data and the action data included in the action history data when a set of the state data, the action data, and the reward value has been added to the action history data; and set weightings for the sets of the state data, the action data, and the reward value included in the action history data so that a distribution of coordinate points indicated by the feature amount vectors in a feature amount space approaches a specified distribution. 7. The machine learning apparatus according to claim 1, wherein the CPU is operable to: use the action history data as learning data to generate, through machine learning, a predictor that predicts, from state data expressing a state in which the agent is present at a present time and action data expressing an action taken by the agent at the present time, state data expressing a state of an agent at a next time following the present time; and input, into the predictor, state data for the present time and action data to predict state data expressing a state of the agent at the next time, and input, into the reward estimator, the state data expressing the state of the agent at the next time and action data expressing an action that can be taken by the agent in the state at the next time to estimate a reward value obtained by the agent as a result of taking the action. 8. The machine learning apparatus according to claim 1, wherein the CPU is operable to: use the action history data as learning data to generate, through machine learning, a predictor that predicts, from state data expressing a state in which the agent is present at a present time and action data expressing an action taken by the agent at the present time, state data expressing a state of an agent at a next time following the present time; execute a process that inputs state data at time t0, if a present time is expressed as time t0, and action data into the predictor to predict state data expressing a state of the agent at a next time t1; successively execute a process that inputs state data for a time tk (where k=1 to (n−1) and n≧2) and action data expressing an action that can be taken by the agent at time tk into the predictor to predict state data expressing the state of the agent at a time tk+1; and input predicted state data expressing a state of the agent at a time tn and action data expressing an action that can be taken by the agent in the state at time tn into the reward estimator to estimate a reward value obtained by the agent as a result of taking the action. 9. The machine learning apparatus according to claim 1, wherein the CPU is operable to use action history data, which includes state data expressing a state of a plurality of agents, action data expressing actions taken by respective agents in the state, and reward values expressing rewards obtained by the respective agents as a result of the actions, as learning data to generate, through machine learning, to estimate a reward value from the inputted state data and action data. 10. An information processing method comprising: learning from action history data that includes state data, action data, and reward value, wherein the state data expressing a state of an agent, the action data expressing an action taken by the agent in the state, and the reward value expressing a reward obtained by the agent as a result of the action; estimating a plurality of reward values based on current state data and a plurality of action data expressing actions that can be taken next by the agent; selecting, among the actions, one action that has a highest estimated reward value; and executing the action that has a highest estimated reward value. 11. A non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions for causing a computer to perform steps comprising: learning from action history data that includes state data, action data, and reward value, wherein the state data expressing a state of an agent, the action data expressing an action taken by the agent in the state, and the reward value expressing a reward obtained by the agent as a result of the action; estimating a plurality of reward values based on current state data and a plurality of action data expressing actions that can be taken next by the agent; selecting, among the actions, one action that has a highest estimated reward value; and executing the action that has a highest estimated reward value.