In this paper, autonomous learning of reward distribution in multi-agent reinforcement learning was applied to the 4 player game named “not100”. In this game, more shrewd tactics to cooperate with the other agents is required for each agent than the other tasks that the learning was applied

, and for all reward distributions with bounded support. Keywords: bandit problems, adaptive allocation rules, finite horizon regret 1.

Abstract. A novel approach for the reward distribution in multi-agent reinforcement learning is proposed. The agent who gets a reward gives a part of it to the other agents. If an agent gives a part of its own reward to the other ones, they may help the agent to get more reward. There may be some

-generating functions of the arm reward distributions are properly bounded, the optimal logarithmic order of the regret can be achieved by DSEE. The condition on the reward distributions can be gradually relaxed at a cost of a higher (nevertheless, sublinear) regret order: for any positive integer p, O(T 1/p) regret

). With function approximation, two ways of formulating the agent's objective are useful. One is the average reward formulation, in which policies are ranked according to their long-term expected reward per step, ρ(π): where d π (s) = lim t→∞ P r {s t = s|s 0 , π} is the stationary distribution of states

distribution in these nodes is modeled by an opponent model that predicts the actions of the opponents. We propose a modification of the standard MCTS selection and backpropagation strategies that explicitly model and exploit the uncertainty of sampled expected values. The new strategies are evaluated as a

We develop a theoretical framework that shows how mesencephalic dopamine systems could distribute to their targets a signal that represents information about future expectations. In particular, we show how activity in the cerebral cortex can make predictions about future receipt of reward and how

and performance-based rewards. Mechanisms based on public key encryption technologies are presented which make it possible to distribute individual rewards to anonymous contributors, guarantee that only the contributor can claim a reward for her contribution, verify that a reward has been distributed, and be able

are sensitive to other individuals ’ efforts and payoffs. In a token exchange task we tested both behavioral responses to inequity in the quality of reward (preferred versus non-preferred food) and to the absence of reward in the presence of a rewarded partner, in 5 pairs of corvids (6 crows, 4 ravens). Birds

Poker playing computer bots can be divided into two categories. There are the game-theoretic bots, that play according to a strategy that gives rise to a Nash equilibrium. These bots are impossible to beat, but are also not able to exploit non-optimalities in their opponents. The other type of bot is the exploiting bot that employs game tree search and opponent modeling techniques to discover and exploit weaknesses of