5 citations · 17 across the 18 of their papers we have counts for
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Language is Power: Representing States Using Natural Language in Reinforcement Learning
Erez Schwartz, Guy Tennenholtz, Chen Tessler +1
Recent advances in reinforcement learning have shown its potential to tackle complex real-life tasks. However, as the dimensionality of the task increases, reinforcement learning m…
Never Worse, Mostly Better: Stable Policy Improvement in Deep Reinforcement Learning
Pranav Khanna, Guy Tennenholtz, Nadav Merlis +2
In recent years, there has been significant progress in applying deep reinforcement learning (RL) for solving challenging problems across a wide variety of domains. Nevertheless, c…
Off-Policy Evaluation in Partially Observable Environments
Guy Tennenholtz, Shie Mannor, Uri Shalit
This work studies the problem of batch off-policy evaluation for Reinforcement Learning in partially observable environments. Off-policy evaluation under partial observability is i…
Distributional Policy Optimization: An Alternative Approach for Continuous Control
Chen Tessler, Guy Tennenholtz, Shie Mannor
We identify a fundamental problem in policy gradient-based methods in continuous control. As policy gradient methods require the agent's underlying probability distribution, they l…
The Natural Language of Actions
Guy Tennenholtz, Shie Mannor
We introduce Act2Vec, a general framework for learning context-based action representation for Reinforcement Learning. Representing actions in a vector space help reinforcement lea…