Abstract: A reinforcement learning system comprises an environment (having multiple possible states), and agent, and a policy learner.. The agent is arranged to receive state information indicative of a current environment state and generate an action signal dependent on the state information and a policy associated with the agent, where the action signal is operable to cause an environment-state change. The agent is further arranged to generate experience data dependent on the state information and information conveyed by the action signal. The policy learner is configured to process the experience data in order to update the policy associated with the agent. The reinforcement learning system further comprises a probabilistic model arranged to generate, dependent on the current state of the environment, probabilistic data relating to future states of the environment, and the agent is further arranged to generate the action signal in dependence on the probabilistic data.

Abstract: A reinforcement learning system comprises an environment (having multiple possible states), and agent, and a policy learner.. The agent is arranged to receive state information indicative of a current environment state and generate an action signal dependent on the state information and a policy associated with the agent, where the action signal is operable to cause an environment-state change. The agent is further arranged to generate experience data dependent on the state information and information conveyed by the action signal. The policy learner is configured to process the experience data in order to update the policy associated with the agent. The reinforcement learning system further comprises a probabilistic model arranged to generate, dependent on the current state of the environment, probabilistic data relating to future states of the environment, and the agent is further arranged to generate the action signal in dependence on the probabilistic data.