MARL Topics

RL in ICML2017 http://sotetsuk.hatenablog.com/entry/icml2017rl
* model based deep reinforcement learning
    * tutorial DRL
    * prediction: end-to-end learning and planning
    * prediction and control with temporal segment models
    * end-to-end differentiable adversarial imitation learning
    * combining model-based and model-free updates for trajectory-centric RL
* model-free deep reinforcement learning: DQN, A3C
* Softmax optimisation
    * RL with deep energy-based policies
    * an alternative softmax operator for RL
    * Bridging the gap between value and policy based RL
    * equivalence between policy gradients and soft q-learning
* hierarchy
    * a laplacian framework for option discovery in RL
    * Feudal networks for hierarchical RL
    * Unifying Task specification in RL
* exploration
    * curiosity-driven exploration by self-supervised prediction
    * count-based exploration with neural density models
* end-to-end learning
    * end-to-end differentiable adversarial imitation learning
    * the prediction: end-to-end learning and planning
    * feudal networks for hierarchical RL
    * zero-shot task generalization with multi-task DRL
* Transfer/Zero-shot learning
    * robust adversarial RL
    * DARLA: improving zero-shot Transfer in RL
    * zero-shot task generalization with multi-task DRL
* practical exploration
    * fairness in RL
    * constrained policy optimization
* Heuristic Teaching
    * interactive learning from policy-dependent human feedback
    * modular multitask RL with policy sketches
    * zero-shot task generalization with multitask DRL
* Bias and variance of Policy off learning
    * Data-efficient policy evaluation through behavior policy search
    * stochastic variance reduction methods for policy evaluation
    * optimal and adaptive off-policy evaluation in contextual bandits
    * consistent on-line off-policy evaluation
* Categorical DQN
    * a distributional perspective on reinforcement learning
* beta policy
    * improving stochastic policy gradients in continuous control with DRL using the beta distribution
* to find option by using PVF

* DDPG(deep determinastic policy gradients)
* efficient exploration https://arxiv.org/pdf/1507.00814v3.pdf , https://arxiv.org/pdf/1606.01868v1.pdf
* life-long learning: hierarchical architecture for the option framework(options = a sequence of actions with clear initiation and termination)
* bayesian RL:  http://www.cse.chalmers.se/~chrdimi/downloads/book.pdf
* inverse RL
* predictive state RL
* markov decision process
* planning by dynamic programming
* model-free prediction
* model free control
* value function approximation
* policy gradient methods
* safe RL http://www.jmlr.org/papers/volume16/garcia15a/garcia15a.pdf
* Topics of Active Research Topics of Active Research in Reinforcement Learning in Reinforcement Learning Relevant to Spoken Relevant to Spoken Dialogue Systems    https://cs.uwaterloo.ca/~ppoupart/publications/presentations/aaai-dialogue-workshop-rl.pdf
* open AI blog: https://blog.openai.com/
* open AI GitHub Repo: https://github.com/openai/baselines
* RL basic theory: http://www.wildml.com/2016/10/learning-reinforcement-learning/
* RL training: https://github.com/dennybritz/reinforcement-learning
* RL research papers: https://medium.com/@joshdotai/deep-reinforcement-learning-papers-a2167c136fc7
* DRL: research papers: https://github.com/junhyukoh/deep-reinforcement-learning-papers
* deep mind publications: https://deepmind.com/research/publications/