By Peter Auer (auth.), Scott Sanner, Marcus Hutter (eds.)
This ebook constitutes revised and chosen papers of the ninth ecu Workshop on Reinforcement studying, EWRL 2011, which happened in Athens, Greece in September 2011. The papers awarded have been rigorously reviewed and chosen from forty submissions. The papers are equipped in topical sections on-line reinforcement studying, studying and exploring MDPs, functionality approximation tools for reinforcement studying, macro-actions in reinforcement studying, coverage seek and limits, multi-task and move reinforcement studying, multi-agent reinforcement studying, apprenticeship and inverse reinforcement studying and real-world reinforcement learning.
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Extra info for Recent Advances in Reinforcement Learning: 9th European Workshop, EWRL 2011, Athens, Greece, September 9-11, 2011, Revised Selected Papers
In recent years, there has been an increased interest in the case of batch reinforcement learning, whereby the data used for learning how to behave are collected a priori. However in many domains, the case of online reinforcement learning is more relevant than the batch case. An additional complication arising in the online case is that the agent must explore its environment eﬃciently. Hence, one of the central issues in online reinforcement learning is the trade-oﬀ between exploration and exploitation.
Sanner and M. ): EWRL 2011, LNCS 7188, pp. 30–41, 2012. c Springer-Verlag Berlin Heidelberg 2012 Gradient Based Algorithms with Loss Functions 31 – Introduction of a model free and a model based algorithm which performs well on standard reinforcement learning benchmark problems, – Introduction of a model based algorithm which performs well under noisy observations (even when the noise makes the process non-Markov), – Extension to full control and evaluation of GTD algorithms. 1 Related Work The classical methods SARSA(λ) and Q-estimation were introduced  in the tabular reinforcement learning setting and were heuristically extended to linear function approximation.
To our knowledge, this is the ﬁrst approach for learning predictive models that integrates learning and planning in a way that explicitly tackles the problem of exploration and exploitation. Our approach is (loosely) based on the actor-critic framework. Model parameters are estimated in an online fashion, interleaved with steps of data gathering and policy optimization. The agent’s behavior policy for interacting with the environment is derived from the most recently optimized policy, which in turn is obtained from planning with the current model and the data gathered thus far.
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