Reward is enough for convex MDPs
Authors: Tom Zahavy, Brendan O'Donoghue, Guillaume Desjardins, Satinder Singh
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We explore this principle in our experiments. Finally, we show that choosing specific algorithms for the policy and cost players unifies several disparate branches of RL problems, such as apprenticeship learning, constrained MDPs, and pure exploration into a single framework, as we summarize in Table 1. which we explore empirically in Appendix F. Appendix F: Additional Experiments |
| Researcher Affiliation | Industry | Tom Zahavy Deep Mind, London tomzahavy@deepmind.com Brendan O Donoghue Deep Mind, London bodonoghue@deepmind.com Guillaume Desjardins Deep Mind, London gdesjardins@deepmind.com Satinder Singh Deep Mind, London baveja@deepmind.com |
| Pseudocode | Yes | Algorithm 1: meta-algorithm for convex MDPs 1: Input: convex-concave payoff L : K Λ R, algorithms Algλ, Algπ, K N 2: for k = 1, . . . , K do 3: λk = Algλ(d1 π, . . . , dk 1 π ; L) 4: dk π = Algπ( λk) 5: end for 6: Return d K π = 1 K PK k=1 dk π, λK = 1 K PK k=1 λk |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a direct link to a code repository for the methodology described. |
| Open Datasets | No | The paper refers to |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or explicit methodology) needed to reproduce data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings in the main text. |